% Use this file for citations not found in the ACL Anthology (contained in "anthology.bib").
@article{goodman2001bit,
  title={A bit of progress in language modeling},
  author={Goodman, Joshua T},
  journal={Computer Speech \& Language},
  volume={15},
  number={4},
  pages={403--434},
  year={2001},
  publisher={Elsevier}
}

@mastersthesis{wu2023predicting-key,
    author = {Wu, Yaru},
    title = {Predicting the Unpredictable--Using Language Models to Assess Literary Quality},
    school = {Uppsala University},
    url = {https://www.diva-portal.org/smash/record.jsf?dswid=6901&pid=diva2%3A1769205},
    year = {2023}
}

@article{hermann2015teaching,
  title={Teaching machines to read and comprehend},
  author={Hermann, Karl Moritz and Kocisky, Tomas and Grefenstette, Edward and Espeholt, Lasse and Kay, Will and Suleyman, Mustafa and Blunsom, Phil},
  journal={Advances in neural information processing systems},
  volume={28},
  year={2015}
}

@article{shannon1949communication,
  title={Communication in the presence of noise},
  author={Shannon, Claude E},
  journal={Proceedings of the IRE},
  volume={37},
  number={1},
  pages={10--21},
  year={1949},
  publisher={IEEE}
}

@article{biber2011historical,
  title={The historical shift of scientific academic prose in English towards less explicit styles of expression},
  author={Biber, Douglas and Gray, Bethany},
  journal={Researching specialized languages},
  volume={47},
  number={11},
  year={2011}
}

@inproceedings{bizzoni2023good,
  title={Good Reads and Easy Novels: Readability and Literary Quality in a Corpus of US-published Fiction},
  author={Bizzoni, Yuri and Moreira, Pascale and Dwenger, Nicole and Lassen, Ida and Thomsen, Mads and Nielbo, Kristoffer},
  booktitle={Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)},
  pages={42--51},
  year={2023}
}

@article{weigel2016literature,
  title={Literature, Literary Criticism and the Historical Index of the Readability of Literary Texts},
  author={Weigel, Sigrid},
  journal={Social Sciences in China},
  volume={37},
  number={3},
  pages={175--185},
  year={2016},
  publisher={Taylor \& Francis}
}

@inproceedings{kao2012computational,
  title={A computational analysis of style, affect, and imagery in contemporary poetry},
  author={Kao, Justine and Jurafsky, Dan},
  booktitle={Proceedings of the NAACL-HLT 2012 workshop on computational linguistics for literature},
  pages={8--17},
  year={2012}
}

@article{mcintosh1975quantities,
  title={Quantities of Qualities: Nominal Style and the Novel},
  author={McIntosh, Carey},
  journal={Studies in Eighteenth-Century Culture},
  volume={4},
  number={1},
  pages={139--153},
  year={1975},
  publisher={Johns Hopkins University Press}
}

@article{degaetano2019typical,
  title={Typical linguistic patterns of English history texts from the eighteenth to the nineteenth century},
  author={Degaetano-Ortlieb, Stefania and Menzel, Katrin and Teich, Elke},
  journal={Writing History in Late Modern English: Explorations of the Coru{\~n}a Corpus},
  pages={58--81},
  year={2019},
  publisher={John Benjamins Publishing Company}
}

@article{bizzoni2020linguistic,
  title={Linguistic variation and change in 250 years of English scientific writing: A data-driven approach},
  author={Bizzoni, Yuri and Degaetano-Ortlieb, Stefania and Fankhauser, Peter and Teich, Elke},
  journal={Frontiers in Artificial Intelligence},
  volume={3},
  pages={73},
  year={2020},
  publisher={Frontiers Media SA}
}

@inproceedings{juzek2020exploring,
  title={Exploring diachronic syntactic shifts with dependency length: the case of scientific English},
  author={Juzek, Tom S and Krielke, Marie-Pauline and Teich, Elke},
  booktitle={Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020)},
  pages={109--119},
  year={2020}
}

@article{huckin1993stylistic,
  title={Stylistic prescriptivism vs. expert practice},
  author={Huckin, Thomas N},
  journal={Discourse and Writing/R{\'e}dactologie},
  volume={11},
  number={2},
  pages={17--Jan},
  year={1993}
}

@article{bostian1983active,
  title={How active, passive and nominal styles affect readability of science writing},
  author={Bostian, Lloyd R.},
  journal={Journalism quarterly},
  volume={60},
  number={4},
  pages={635--670},
  year={1983},
  publisher={SAGE Publications Sage CA: Los Angeles, CA}
}



@inproceedings{ashok2013success,
  title={Success with style: Using writing style to predict the success of novels},
  author={Ashok, Vikas Ganjigunte and Feng, Song and Choi, Yejin},
  booktitle={Proceedings of the 2013 conference on empirical methods in natural language processing},
  pages={1753--1764},
  year={2013}
}

@book{strunk2007elements,
  title={The Elements of Style Illustrated},
  author={Strunk Jr, William and White, Elwyn Brooks},
  year={2007},
  publisher={Penguin}
}

@book{goldberg2022neural,
  title={Neural network methods for natural language processing},
  author={Goldberg, Yoav},
  year={2022},
  publisher={Springer Nature}
}


@inproceedings{hao2020probabilistic,
    title = "Probabilistic Predictions of People Perusing: Evaluating Metrics of Language Model Performance for Psycholinguistic Modeling",
    author = "Hao, Yiding  and
      Mendelsohn, Simon  and
      Sterneck, Rachel  and
      Martinez, Randi  and
      Frank, Robert",
    editor = "Chersoni, Emmanuele  and
      Jacobs, Cassandra  and
      Oseki, Yohei  and
      Pr{\'e}vot, Laurent  and
      Santus, Enrico",
    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.cmcl-1.10",
    doi = "10.18653/v1/2020.cmcl-1.10",
    pages = "75--86",
    abstract = "By positing a relationship between naturalistic reading times and information-theoretic surprisal, surprisal theory (Hale, 2001; Levy, 2008) provides a natural interface between language models and psycholinguistic models. This paper re-evaluates a claim due to Goodkind and Bicknell (2018) that a language model{'}s ability to model reading times is a linear function of its perplexity. By extending Goodkind and Bicknell{'}s analysis to modern neural architectures, we show that the proposed relation does not always hold for Long Short-Term Memory networks, Transformers, and pre-trained models. We introduce an alternate measure of language modeling performance called predictability norm correlation based on Cloze probabilities measured from human subjects. Our new metric yields a more robust relationship between language model quality and psycholinguistic modeling performance that allows for comparison between models with different training configurations.",
}

@article{oh2023does,
  title={Why does surprisal from larger transformer-based language models provide a poorer fit to human reading times?},
  author={Oh, Byung-Doh and Schuler, William},
  journal={Transactions of the Association for Computational Linguistics},
  volume={11},
  pages={336--350},
  year={2023},
  publisher={MIT Press}
}

@inproceedings{sennrich2015neural,
    title = "Neural Machine Translation of Rare Words with Subword Units",
    author = "Sennrich, Rico  and
      Haddow, Barry  and
      Birch, Alexandra",
    editor = "Erk, Katrin  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P16-1162",
    doi = "10.18653/v1/P16-1162",
    pages = "1715--1725",
}

@article{radford2019language,
  title={Language models are unsupervised multitask learners},
  author={Radford, Alec and Wu, Jeffrey and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya and others},
  journal={OpenAI blog},
  volume={1},
  number={8},
  pages={9},
  year={2019}
}

@inproceedings{cox_long_1984,
	title = {Long range dependence: {A} review},
	booktitle = {Iowa {State} {University}},
	publisher = {Press},
	author = {Cox, Timothy M.},
	year = {1984}
}

@inproceedings{bizzoni2023good,
  title={Good Reads and Easy Novels: Readability and Literary Quality in a Corpus of US-published Fiction},
  author={Bizzoni, Yuri and Moreira, Pascale and Dwenger, Nicole and Lassen, Ida and Thomsen, Mads and Nielbo, Kristoffer},
  booktitle={Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)},
  pages={42--51},
  year={2023}
}

@article{riley_tutorial_2012,
	title = {A tutorial introduction to adaptive fractal analysis},
	volume = {3},
	issn = {1664-042X},
	url = {http://journal.frontiersin.org/article/10.3389/fphys.2012.00371/abstract},
	doi = {10.3389/fphys.2012.00371},
	urldate = {2016-05-19},
	journal = {Frontiers in Physiology},
	author = {Riley, Michael A. and Bonnette, Scott and Kuznetsov, Nikita and Wallot, Sebastian and Gao, Jianbo},
	year = {2012},
	file = {Gao_2012_Adaptive_fractial_analysis.pdf:C\:\\Users\\KLN\\Documents\\gao\\Gao_2012_Adaptive_fractial_analysis.pdf:application/pdf}
}

@book{mandelbrot_fractal_1982,
	address = {San Francisco},
	edition = {Updated ed. edition},
	title = {The {Fractal} {Geometry} of {Nature}},
	isbn = {978-0-7167-1186-5},
	abstract = {Clouds are not spheres, mountains are not cones, and lightening does not travel in a straight line. The complexity of nature's shapes differs in kind, not merely degree, from that of the shapes of ordinary geometry, the geometry of fractal shapes. Now that the field has expanded greatly with many active researchers, Mandelbrot presents the definitive overview of the origins of his ideas and their new applications. The Fractal Geometry of Nature is based on his highly acclaimed earlier work, but has much broader and deeper coverage and more extensive illustrations.},
	language = {English},
	publisher = {Times Books},
	author = {Mandelbrot, Benoit},
	year = {1982}
}

@article{mandelbrot_fractional_1968,
	title = {Fractional {Brownian} {Motions}, {Fractional} {Noises} and {Applications}},
	volume = {10},
	issn = {00361445},
	url = {http://www.jstor.org/stable/2027184},
	number = {4},
	journal = {SIAM Review},
	author = {Mandelbrot, Benoit B. and Ness, John W. Van},
	year = {1968},
	pages = {422--437}
}

@book{beran_statistics_1994,
	address = {New York},
	edition = {1},
	title = {Statistics for {Long}-{Memory} {Processes}},
	isbn = {978-0-412-04901-9},
	abstract = {Statistical Methods for Long Term Memory Processes covers the diverse statistical methods and applications for data with long-range dependence. Presenting material that previously appeared only in journals, the author provides a concise and effective overview of probabilistic foundations, statistical methods, and applications. The material emphasizes basic principles and practical applications and provides an integrated perspective of both theory and practice. This book explores data sets from a wide range of disciplines, such as hydrology, climatology, telecommunications engineering, and high-precision physical measurement. The data sets are conveniently compiled in the index, and this allows readers to view statistical approaches in a practical context. Statistical Methods for Long Term Memory Processes also supplies S-PLUS programs for the major methods discussed. This feature allows the practitioner to apply long memory processes in daily data analysis. For newcomers to the area, the first three chapters provide the basic knowledge necessary for understanding the remainder of the material. To promote selective reading, the author presents the chapters independently. Combining essential methodologies with real-life applications, this outstanding volume is and indispensable reference for statisticians and scientists who analyze data with long-range dependence.},
	language = {English},
	publisher = {Chapman and Hall/CRC},
	author = {Beran, Jan},
	month = oct,
	year = {1994}
}

@article{drobot2013affective,
  title={Affective Narratology. The Emotional Structure of Stories},
  author={Drobot, Irina-Ana},
  journal={Philologica Jassyensia},
  volume={9},
  number={2},
  pages={338},
  year={2013},
  publisher={" A. Philippide" Institute of Romanian Philology," A. Philippide" Cultural~…}
}

@book{mandelbrot_fractals_1997,
	address = {New York},
	edition = {1997 edition},
	title = {Fractals and {Scaling} in {Finance}: {Discontinuity}, {Concentration}, {Risk}. {Selecta} {Volume} {E}},
	isbn = {978-0-387-98363-9},
	shorttitle = {Fractals and {Scaling} in {Finance}},
	abstract = {Mandelbrot is world famous for his creation of the new mathematics of fractal geometry. Yet few people know that his original field of applied research was in econometrics and financial models, applying ideas of scaling and self-similarity to arrays of data generated by financial analyses. This book brings together his original papers as well as many original chapters specifically written for this book.},
	language = {English},
	publisher = {Springer},
	author = {Mandelbrot, Benoit B.},
	month = sep,
	year = {1997}
}

@article{kuznetsov_adaptive_2013,
	title = {Adaptive {Fractal} {Analysis} {Reveals} {Limits} to {Fractal} {Scaling} in {Center} of {Pressure} {Trajectories}},
	volume = {41},
	issn = {0090-6964, 1573-9686},
	url = {http://link.springer.com/10.1007/s10439-012-0646-9},
	doi = {10.1007/s10439-012-0646-9},
	abstract = {Fractal time series analysis methods are commonly used for analyzing center of pressure (COP) signals with the goal of revealing the underlying neuromuscular processes for upright stance control. The use of fractal methods is often coupled with the assumption that the COP is an instance of fractional Gaussian noise (fGn) or fractional Brownian motion (fBm). Our purpose was to evaluate the applicability of the fGn–fBm framework to the COP in light of several characteristics of COP signals revealed by a new method, adaptive fractal analysis (AFA). AFA quantiﬁes how the variance of the residuals to ﬁts of a globally smooth trend signal scales with the time scale at which the ﬁts are performed. Application of AFA to COP signals revealed that there are potentially three fractal scaling regions in the COP as opposed to one as expected from a pure fGn or fBm process. The scaling region at the fastest scale was antipersistent and spanned {\textasciitilde}30–90 ms, the intermediate was persistent and spanned {\textasciitilde}200 ms–1.9 s, and the slowest was anti-persistent and spanned {\textasciitilde}5–40 s. The intermediate fractal scaling region was the most clearly deﬁned, but it only contributed around 11\% of the total spectral energy of the COP signal, indicating that other features of the COP signal contribute more importantly to the overall dynamics. Also, more than half of the Hurst exponents estimated for the intermediate region were greater than the theoretically expected range [0,1] for fGn–fBm processes. These results suggest the fGn–fBm framework is not appropriate for modeling COP signals. ON–OFF intermittency might provide a better modeling framework for the COP, and multiscale approaches may be more appropriate for analyzing COP data.},
	language = {en},
	number = {8},
	urldate = {2018-04-02},
	journal = {Annals of Biomedical Engineering},
	author = {Kuznetsov, Nikita and Bonnette, Scott and Gao, Jianbo and Riley, Michael A.},
	month = aug,
	year = {2013},
	pages = {1646--1660},
	file = {Kuznetsov et al. - 2013 - Adaptive Fractal Analysis Reveals Limits to Fracta.pdf:/home/knielbo/Documents/articles/fractals/Kuznetsov et al. - 2013 - Adaptive Fractal Analysis Reveals Limits to Fracta.pdf:application/pdf}
}

@inproceedings{rubino2016modeling,
  title={Modeling diachronic change in scientific writing with information density},
  author={Rubino, Raphael and Degaetano-Ortlieb, Stefania and Teich, Elke and van Genabith, Josef},
  booktitle={Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  pages={750--761},
  year={2016}
}

@article{eke_fractal_2002,
	title = {Fractal characterization of complexity in temporal physiological signals},
	volume = {23},
	url = {http://stacks.iop.org/0967-3334/23/i=1/a=201},
	abstract = {This review first gives an overview on the concept of fractal geometry with definitions and explanations of the most fundamental properties of fractal structures and processes like self-similarity, power law scaling relationship, scale invariance, scaling range and fractal dimensions. Having laid down the grounds of the basics in terminology and mathematical formalism, the authors systematically introduce the concept and methods of monofractal time series analysis. They argue that fractal time series analysis cannot be done in a conscious, reliable manner without having a model capable of capturing the essential features of physiological signals with regard to their fractal analysis. They advocate the use of a simple, yet adequate, dichotomous model of fractional Gaussian noise (fGn) and fractional Brownian motion (fBm). They demonstrate the importance of incorporating a step of signal classification according to the fGn/fBm model prior to fractal analysis by showing that missing out on signal class can result in completely meaningless fractal estimates. Limitation and precision of various fractal tools are thoroughly described and discussed using results of numerical experiments on ideal monofractal signals. Steps of a reliable fractal analysis are explained. Finally, the main applications of fractal time series analysis in biomedical research are reviewed and critically evaluated.},
	number = {1},
	journal = {Physiological Measurement},
	author = {Eke, A. and Herman, P. and Kocsis, L. and Kozak, L. R.},
	year = {2002},
	pages = {R1},
	file = {eke_physiological_signals_2002.pdf:/home/knielbo/Documents/articles/fractals/eke_physiological_signals_2002.pdf:application/pdf}
}



@article{gao_facilitating_2011,
	title = {Facilitating Joint Chaos and Fractal Analysis of Biosignals through Nonlinear Adaptive Filtering},
	volume = {6},
	issn = {1932-6203},
	doi = {10.1371/journal.pone.0024331},
	language = {en},
	number = {9},
	urldate = {2017-10-18},
	journal = {PLoS ONE},
	author = {Gao, Jianbo and Hu, Jing and Tung, Wen-wen},
	editor = {Perc, Matjaz},
	month = sep,
	year = {2011},
}

@article{cannon_evaluating_1997,
	title = {Evaluating scaled windowed variance methods for estimating the {Hurst} coefficient of time series},
	volume = {241},
	number = {3-4},
	journal = {Physica A: Statistical Mechanics and its Applications},
	author = {Cannon, Michael J. and Percival, Donald B. and Caccia, David C. and Raymond, Gary M. and Bassingthwaighte, James B.},
	year = {1997},
	pages = {606--626},
	file = {cannon_windowvariance_1997.pdf:/home/knielbo/Documents/articles/fractals/cannon_windowvariance_1997.pdf:application/pdf}
}

@article{kantelhardt_multifractal_2002,
	title = {Multifractal detrended fluctuation analysis of nonstationary time series},
	volume = {316},
	number = {1-4},
	journal = {Physica A: Statistical Mechanics and its Applications},
	author = {Kantelhardt, Jan W. and Zschiegner, Stephan A. and Koscielny-Bunde, Eva and Havlin, Shlomo and Bunde, Armin and Stanley, H. Eugene},
	year = {2002},
	pages = {87--114},
	file = {Kantelhardt_dfa_2002.pdf:/home/knielbo/Documents/articles/fractals/Kantelhardt_dfa_2002.pdf:application/pdf}
}

@article{hu_effect_2001,
	title = {Effect of trends on detrended fluctuation analysis},
	volume = {64},
	issn = {1063-651X, 1095-3787},
	url = {https://link.aps.org/doi/10.1103/PhysRevE.64.011114},
	doi = {10.1103/PhysRevE.64.011114},
	language = {en},
	number = {1},
	urldate = {2018-02-04},
	journal = {Physical Review E},
	author = {Hu, Kun and Ivanov, Plamen Ch. and Chen, Zhi and Carpena, Pedro and Eugene Stanley, H.},
	month = jun,
	year = {2001},
	file = {hu_trends_2002.pdf:/home/knielbo/Documents/articles/fractals/hu_trends_2002.pdf:application/pdf}
}

@article{chen_effect_2005,
	title = {Effect of nonlinear filters on detrended fluctuation analysis},
	volume = {71},
	url = {https://link.aps.org/doi/10.1103/PhysRevE.71.011104},
	doi = {10.1103/PhysRevE.71.011104},
	number = {1},
	journal = {Phys. Rev. E},
	author = {Chen, Zhi and Hu, Kun and Carpena, Pedro and Bernaola-Galvan, Pedro and Stanley, H. Eugene and Ivanov, Plamen Ch.},
	month = jan,
	year = {2005},
	pages = {011104},
	file = {chen_nonlin_filter_2005.PDF:/home/knielbo/Documents/articles/fractals/chen_nonlin_filter_2005.PDF:application/pdf}
}

@article{peng_mosaic_1994,
	title = {Mosaic organization of {DNA} nucleotides},
	volume = {49},
	number = {2},
	journal = {Physical review e},
	author = {Peng, C.-K. and Buldyrev, Sergey V. and Havlin, Shlomo and Simons, Michael and Stanley, H. Eugene and Goldberger, Ary L.},
	year = {1994},
	pages = {1685},
	file = {peng_dfa_1994.pdf:/home/knielbo/Documents/articles/fractals/peng_dfa_1994.pdf:application/pdf}
}

@article{hu_multifractal_2009,
	title = {Multifractal analysis of sunspot time series: the effects of the 11-year cycle and {Fourier} truncation},
	volume = {2009},
	issn = {1742-5468},
	shorttitle = {Multifractal analysis of sunspot time series},
	url = {http://stacks.iop.org/1742-5468/2009/i=02/a=P02066?key=crossref.879d2c42ec8804831202df82da8d7a1a},
	doi = {10.1088/1742-5468/2009/02/P02066},
	number = {02},
	urldate = {2018-02-04},
	journal = {Journal of Statistical Mechanics: Theory and Experiment},
	author = {Hu, Jing and Gao, Jianbo and Wang, Xingsong},
	month = feb,
	year = {2009},
	pages = {P02066},
	file = {Hu_sunspot_2009.pdf:/home/knielbo/Documents/articles/fractals/Hu_sunspot_2009.pdf:application/pdf}
}

@inproceedings{cordeiro_fractal_2015,
	location = {Cham},
	title = {Fractal Beauty in Text},
	isbn = {978-3-319-23485-4},
	doi = {10.1007/978-3-319-23485-4_80},
	series = {Lecture Notes in Computer Science},
	abstract = {This paper assesses if text possesses fractal properties, namely if several attributes that characterize sentences are self-similar. In order to do that, seven corpora were analyzed using several statistical tools, so as to determine if the empirical sequences for the attributes were Gaussian and self-similar. The Kolmogorov-Smirnov goodness-of-fit test and two Hurst parameter estimators were employed. The results show that there is a fractal beauty in the text produced by humans and suggest that its quality is directly proportional to the self-similarity degree.},
	pages = {796--802},
	booktitle = {Progress in Artificial Intelligence},
	publisher = {Springer International Publishing},
	author = {Cordeiro, João and Inácio, Pedro R. M. and Fernandes, Diogo A. B.},
	editor = {Pereira, Francisco and Machado, Penousal and Costa, Ernesto and Cardoso, Amílcar},
	date = {2015},
	year = {2015},
	langid = {english},
	keywords = {Self-similarity in text, Statistical linguistic studies},
}

@article{kim_survey_2018,
	title = {A survey on sentiment and emotion analysis for computational literary studies},
	journal = {{arXiv} preprint {arXiv}:1808.03137},
	author = {Kim, Evgeny and Klinger, Roman},
	year = {2018},
}

@article{mohammad2013nrc,
  title={{NRC} emotion lexicon},
  author={Mohammad, Saif M and Turney, Peter D},
  journal={National Research Council, Canada},
  volume={2},
  year={2013},
    pages = {334}
}

@incollection{cambria2017affective,
  title={Affective computing and sentiment analysis},
  author={Cambria, Erik and Das, Dipankar and Bandyopadhyay, Sivaji and Feraco, Antonio},
  booktitle={A practical guide to sentiment analysis},
  pages={1--10},
  year={2017},
  publisher={Springer}
}

@inproceedings{brooke2015gutentag,
  title={GutenTag: an NLP-driven tool for digital humanities research in the Project Gutenberg corpus},
  author={Brooke, Julian and Hammond, Adam and Hirst, Graeme},
  booktitle={Proceedings of the Fourth Workshop on Computational Linguistics for Literature},
  pages={42--47},
  year={2015}
}

@misc{jockers2017syuzhet,
  title={Syuzhet: Extracts Sentiment and Sentiment-Derived Plot Arcs from Text (Version 1.0. 1)},
  author={Jockers, Matthew},
  year={2017}
}

@inproceedings{gao2016multiscale,
  title={A multiscale theory for the dynamical evolution of sentiment in novels},
  author={Gao, Jianbo and Jockers, Matthew L and Laudun, John and Tangherlini, Timothy},
  booktitle={2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC)},
  pages={1--4},
  year={2016},
  organization={IEEE}
}

@article{reagan_emotional_2016,
	title = {The emotional arcs of stories are dominated by six basic shapes},
	volume = {5},
	issn = {2193-1127},
	doi = {10.1140/epjds/s13688-016-0093-1},
	number = {1},
	journal = {{EPJ} Data Science},
	author = {Reagan, Andrew J. and Mitchell, Lewis and Kiley, Dilan and Danforth, Christopher M. and Dodds, Peter Sheridan},
	month = nov,
	year = {2016},
	pages = {31},
}



@article{mantyla_evolution_2018,
	title = {The evolution of sentiment analysis—A review of research topics, venues, and top cited papers},
	volume = {27},
	issn = {1574-0137},
	url = {https://www.sciencedirect.com/science/article/pii/S1574013717300606},
	doi = {10.1016/j.cosrev.2017.10.002},
	abstract = {Sentiment analysis is one of the fastest growing research areas in computer science, making it challenging to keep track of all the activities in the area. We present a computer-assisted literature review, where we utilize both text mining and qualitative coding, and analyze 6996 papers from Scopus. We find that the roots of sentiment analysis are in the studies on public opinion analysis at the beginning of 20th century and in the text subjectivity analysis performed by the computational linguistics community in 1990’s. However, the outbreak of computer-based sentiment analysis only occurred with the availability of subjective texts on the Web. Consequently, 99\% of the papers have been published after 2004. Sentiment analysis papers are scattered to multiple publication venues, and the combined number of papers in the top-15 venues only represent ca. 30\% of the papers in total. We present the top-20 cited papers from Google Scholar and Scopus and a taxonomy of research topics. In recent years, sentiment analysis has shifted from analyzing online product reviews to social media texts from Twitter and Facebook. Many topics beyond product reviews like stock markets, elections, disasters, medicine, software engineering and cyberbullying extend the utilization of sentiment analysis.},
	pages = {16--32},
	journaltitle = {Computer Science Review},
	shortjournal = {Computer Science Review},
	author = {Mäntylä, Mika V. and Graziotin, Daniel and Kuutila, Miikka},
	urldate = {2022-02-23},
	date = {2018-02-01},
	year = {2018},
	langid = {english},
	keywords = {Bibliometric study, Latent Dirichlet Allocation, Literature review, Opinion mining, Qualitative analysis, Sentiment analysis, Text mining, Topic modeling},
	
}

@article{mohseni_fractality_2021,
	title = {Fractality and Variability in Canonical and Non-Canonical English Fiction and in Non-Fictional Texts},
	volume = {12},
	issn = {1664-1078},
	url = {https://www.frontiersin.org/article/10.3389/fpsyg.2021.599063},
	abstract = {This study investigates global properties of three categories of English text: canonical fiction, non-canonical fiction, and non-fictional texts. The central hypothesis of the study is that there are systematic differences with respect to structural design features between canonical and non-canonical fiction, and between fictional and non-fictional texts. To investigate these differences, we compiled a corpus containing texts of the three categories of interest, the Jena Corpus of Expository and Fictional Prose ({JEFP} Corpus). Two aspects of global structure are investigated, variability and self-similar (fractal) patterns, which reflect long-range correlations along texts. We use four types of basic observations, (i) the frequency of {POS}-tags per sentence, (ii) sentence length, (iii) lexical diversity, and (iv) the distribution of topic probabilities in segments of texts. These basic observations are grouped into two more general categories, (a) the lower-level properties (i) and (ii), which are observed at the level of the sentence (reflecting linguistic decoding), and (b) the higher-level properties (iii) and (iv), which are observed at the textual level (reflecting comprehension/integration). The observations for each property are transformed into series, which are analyzed in terms of variance and subjected to Multi-Fractal Detrended Fluctuation Analysis ({MFDFA}), giving rise to three statistics: (i) the degree of fractality (H), (ii) the degree of multifractality (D), i.e., the width of the fractal spectrum, and (iii) the degree of asymmetry (A) of the fractal spectrum. The statistics thus obtained are compared individually across text categories and jointly fed into a classification model (Support Vector Machine). Our results show that there are in fact differences between the three text categories of interest. In general, lower-level text properties are better discriminators than higher-level text properties. Canonical fictional texts differ from non-canonical ones primarily in terms of variability in lower-level text properties. Fractality seems to be a universal feature of text, slightly more pronounced in non-fictional than in fictional texts. On the basis of our results obtained on the basis of corpus data we point out some avenues for future research leading toward a more comprehensive analysis of textual aesthetics, e.g., using experimental methodologies.},
	journaltitle = {Frontiers in Psychology},
	author = {Mohseni, Mahdi and Gast, Volker and Redies, Christoph},
	urldate = {2022-02-23},
	year = {2021},
}

@article{kousha_goodreads_2017,
	title = {Goodreads reviews to assess the wider impacts of books},
	volume = {68},
	issn = {2330-1643},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/asi.23805},
	doi = {10.1002/asi.23805},
	abstract = {Although peer-review and citation counts are commonly used to help assess the scholarly impact of published research, informal reader feedback might also be exploited to help assess the wider impacts of books, such as their educational or cultural value. The social website Goodreads seems to be a reasonable source for this purpose because it includes a large number of book reviews and ratings by many users inside and outside of academia. To check this, Goodreads book metrics were compared with different book-based impact indicators for 15,928 academic books across broad fields. Goodreads engagements were numerous enough in the arts (85\% of books had at least one), humanities (80\%), and social sciences (67\%) for use as a source of impact evidence. Low and moderate correlations between Goodreads book metrics and scholarly or non-scholarly indicators suggest that reader feedback in Goodreads reflects the many purposes of books rather than a single type of impact. Although Goodreads book metrics can be manipulated, they could be used guardedly by academics, authors, and publishers in evaluations.},
	pages = {2004--2016},
	number = {8},
	journaltitle = {Journal of the Association for Information Science and Technology},
	author = {Kousha, Kayvan and Thelwall, Mike and Abdoli, Mahshid},
	urldate = {2022-02-23},
	year = {2017},
	langid = {english},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/asi.23805},
	file = {Full Text PDF:/home/telmix/Zotero/storage/IEDYV2GT/Kousha et al. - 2017 - Goodreads reviews to assess the wider impacts of b.pdf:application/pdf;Snapshot:/home/telmix/Zotero/storage/W55VMNEB/asi.html:text/html},
}


@book{underwood_distant_2019,
	title = {Distant Horizons: Digital Evidence and Literary Change},
	isbn = {978-0-226-61297-3},
	url = {https://www.degruyter.com/document/doi/10.7208/9780226612973/html},
	shorttitle = {Distant Horizons},
	abstract = {Just as a traveler crossing a continent won’t sense the curvature of the earth, one lifetime of reading can’t grasp the largest patterns organizing literary history. This is the guiding premise behind Distant Horizons , which uses the scope of data newly available to us through digital libraries to tackle previously elusive questions about literature.},
	publisher = {University of Chicago Press},
	author = {Underwood, Ted},
	urldate = {2022-02-23},
	date = {2019-02-14},
	year = {2019},
	langid = {english},
	doi = {10.7208/9780226612973},
	note = {Publication Title: Distant Horizons},
	keywords = {American literature, characterization, cultural analytics, diction, digital humanities, distant reading, English literature, gender, genre, machine learning},
}


@article{hu2021dynamic,
	title = {Dynamic evolution of sentiments in {Never} {Let} {Me} {Go}: {Insights} from multifractal theory and its implications for literary analysis},
	volume = {36},
	issn = {2055-7671},
	doi = {10.1093/llc/fqz092},
	number = {2},
	journal = {Digital Scholarship in the Humanities},
	author = {Hu, Qiyue and Liu, Bin and Thomsen, Mads Rosendahl and Gao, Jianbo and Nielbo, Kristoffer L},
	month = jun,
	year = {2020},
	pages = {322--332},
}


@inproceedings{alm_characteristics_2010,
	title = {Characteristics of high agreement affect annotation in text},
	eventtitle = {Proceedings of the fourth linguistic annotation workshop},
	pages = {118--122},
	author = {Alm, Cecilia Ovesdotter},
	year = {2010},
}

@inproceedings{alm_emotions_2005,
	title = {Emotions from text: machine learning for text-based emotion prediction},
	pages = {579--586},
	booktitle = {Proceedings of human language technology conference and conference on empirical methods in natural language processing},
	author = {Alm, Cecilia Ovesdotter and Roth, Dan and Sproat, Richard},
	year = {2005},
}

@inproceedings{vad-acl2018,
    title = "Obtaining Reliable Human Ratings of Valence, Arousal, and Dominance for 20,000 {E}nglish Words",
    author = "Mohammad, Saif",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P18-1017",
    pages = "174--184",
}

@book{alm2008affect,
  title={Affect in* text and speech},
  author={Alm, Ebba Cecilia Ovesdotter},
  year={2008},
  publisher={University of Illinois at Urbana-Champaign}
}

@article{wilkens2012canons,
  title={Canons, close reading, and the evolution of method},
  author={Wilkens, Matthew},
  journal={Debates in the digital humanities},
  pages={249--58},
  year={2012},
  publisher={University of Minnesota Press Minneapolis}
}

@article{murtagh2014ward,
  title={Ward’s hierarchical agglomerative clustering method: which algorithms implement Ward’s criterion?},
  author={Murtagh, Fionn and Legendre, Pierre},
  journal={Journal of classification},
  volume={31},
  number={3},
  pages={274--295},
  year={2014},
  publisher={Springer}
}

@inproceedings{jain-etal-2017-sentiment,
    title = "Sentiment Analysis: An Empirical Comparative Study of Various Machine Learning Approaches",
    author = "Jain, Swapnil  and
      Malviya, Shrikant  and
      Mishra, Rohit  and
      Tiwary, Uma Shanker",
    booktitle = "Proceedings of the 14th International Conference on Natural Language Processing ({ICON}-2017)",
    month = dec,
    year = "2017",
    address = "Kolkata, India",
    publisher = "NLP Association of India",
    url = "https://aclanthology.org/W17-7515",
    pages = "112--121",
}

@article{li2019exploiting,
    title = "Exploiting {BERT} for End-to-End Aspect-based Sentiment Analysis",
    author = "Li, Xin  and
      Bing, Lidong  and
      Zhang, Wenxuan  and
      Lam, Wai",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D19-5505",
    pages = "34--41",
}

@article{xu2020dombert,
  title={DomBERT: Domain-oriented language model for aspect-based sentiment analysis},
  author={Xu, Hu and Liu, Bing and Shu, Lei and Yu, Philip S},
  journal={arXiv preprint arXiv:2004.13816},
  year={2020}
}

@inproceedings{islam2020domain,
    title = "Domain-Specific Sentiment Lexicons Induced from Labeled Documents",
    author = "Islam, SM Mazharul  and
      Dong, Xin  and
      de Melo, Gerard",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://aclanthology.org/2020.coling-main.578",
    pages = "6576--6587",
}

@article{walsh2021goodreads,
  title={The Goodreads ‘Classics’: A Computational Study of Readers, Amazon, and Crowdsourced Amateur Criticism},
  author={Walsh, Melanie and Antoniak, Maria},
  journal={Journal of Cultural Analytics},
  volume={4},
  pages={243--287},
  year={2021},
  publisher={Department of Languages, Literatures, and Cultures}
}

@book{moretti2013distant,
  title={Distant reading},
  author={Moretti, Franco},
  year={2013},
  publisher={Verso Books}
}

@inproceedings{van2015identifying,
  title={Identifying literary texts with bigrams},
  author={van Cranenburgh, Andreas and Koolen, Corina},
  booktitle={Proceedings of the Fourth Workshop on Computational Linguistics for Literature},
  pages={58--67},
  year={2015}
}

@article{gao2011,
	title = {Facilitating Joint Chaos and Fractal Analysis of Biosignals through Nonlinear Adaptive Filtering},
	volume = {6},
	issn = {1932-6203},
	doi = {10.1371/journal.pone.0024331},
	language = {en},
	number = {9},
	urldate = {2017-10-18},
	journal = {PLoS ONE},
	author = {Gao, Jianbo and Hu, Jing and Tung, Wen-wen},
	editor = {Perc, Matjaz},
	month = sep,
	year = {2011},
}

@article{tung2011,
	title = {Detecting chaos in heavy-noise environments},
	volume = {83},
	issn = {1539-3755, 1550-2376},
	doi = {10.1103/PhysRevE.83.046210},
	language = {en},
	number = {4},
	urldate = {2019-10-14},
	journal = {Physical Review E},
	author = {Tung, Wen-wen and Gao, Jianbo and Hu, Jing and Yang, Lei},
	month = apr,
	year = {2011}
}

@article{bose2015generalized,
  title={Generalized quadratic discriminant analysis},
  author={Bose, Smarajit and Pal, Amita and SahaRay, Rita and Nayak, Jitadeepa},
  journal={Pattern Recognition},
  volume={48},
  number={8},
  pages={2676--2684},
  year={2015},
  publisher={Elsevier}
}

@inproceedings{ho1995random, 
  title={Random decision forests}, 
  author={Ho, Tin Kam}, 
  booktitle={Proceedings of 3rd international conference on document analysis and recognition}, 
  volume={1}, 
  pages={278--282}, 
  year={1995}, 
  organization={IEEE} 
}

@inproceedings{chan1982updating,
  title={Updating formulae and a pairwise algorithm for computing sample variances},
  author={Chan, Tony F and Golub, Gene H and LeVeque, Randall J},
  booktitle={COMPSTAT 1982 5th Symposium held at Toulouse 1982},
  pages={30--41},
  year={1982},
  organization={Springer}
}

@article{swain1977decision,
  title={The decision tree classifier: Design and potential},
  author={Swain, Philip H and Hauska, Hans},
  journal={IEEE Transactions on Geoscience Electronics},
  volume={15},
  number={3},
  pages={142--147},
  year={1977},
  publisher={IEEE}
}

@article{bao2016boosted,
  title={Boosted near-miss under-sampling on SVM ensembles for concept detection in large-scale imbalanced datasets},
  author={Bao, Lei and Juan, Cao and Li, Jintao and Zhang, Yongdong},
  journal={Neurocomputing},
  volume={172},
  pages={198--206},
  year={2016},
  publisher={Elsevier}
}

@article{chawla2002smote,
  title={SMOTE: synthetic minority over-sampling technique},
  author={Chawla, Nitesh V and Bowyer, Kevin W and Hall, Lawrence O and Kegelmeyer, W Philip},
  journal={Journal of artificial intelligence research},
  volume={16},
  pages={321--357},
  year={2002}
}

@inproceedings{mani2003knn,
  title={kNN approach to unbalanced data distributions: a case study involving information extraction},
  author={Mani, Inderjeet and Zhang, I},
  booktitle={Proceedings of workshop on learning from imbalanced datasets},
  volume={126},
  pages={1--7},
  year={2003},
  organization={ICML}
}

@article{bizzoni2022fractal,
  title={Fractal Sentiments and Fairy Tales-Fractal scaling of narrative arcs as predictor of the perceived quality of {Andersen's} fairy tales},
  author={Bizzoni, Yuri and Peura, Telma and Nielbo, Kristoffer and Thomsen, Mads},
  journal={Journal of Data Mining \& Digital Humanities},
  year={2022},
    volume = {{NLP4DH}},
    doi = {10.46298/jdmdh.9154}

}

@inproceedings{hutto2014vader,
  title={{VADER}: A parsimonious rule-based model for sentiment analysis of social media text},
  author={Hutto, Clayton and Gilbert, Eric},
  booktitle={Proceedings of the international AAAI conference on web and social media},
  volume={8},
  number={1},
  pages={216--225},
  year={2014},
    doi = {10.1609/icwsm.v8i1.14550}
}

@inproceedings{bird2006nltk,
  title={NLTK: the natural language toolkit},
  author={Bird, Steven},
  booktitle={Proceedings of the COLING/ACL 2006 Interactive Presentation Sessions},
  pages={69--72},
  year={2006}
}


@inproceedings{textlabs,
  title={US Novel Corpus},
  author={Textual Optic Labs},
  booktitle={University of Chicago}
}

@techreport{textlabscorpus,
  added-at = {2008-02-29T17:14:20.000+0100},
  author = {Long, Hoyt and Roland, Teddy},
  biburl = {https://www.bibsonomy.org/bibtex/260bb0c74c2ecced0632393e47eb64f48/sb3000},
  institution = {Textual Optic Labs, University of Chicago},
  interhash = {119c367841941ad1a8f0db35d9f1c0b9},
  intrahash = {60bb0c74c2ecced0632393e47eb64f48},
  keywords = {corpus linguistics text-mining},
  title = {US Novel Corpus},
  url = {http://icame.uib.no/brown/bcm.html},
  year = 2016
}

@article{cheng2020fleshing,
  title={Fleshing out models of gender in {English-language} novels (1850--2000)},
  author={Cheng, Jonathan},
  journal={Journal of Cultural Analytics},
  volume={5},
  number={1},
  pages={11652},
  year={2020},
  publisher={Department of Languages, Literatures, and Cultures},
    doi = {10.22148/001c.11652}
}

@article{underwood2018transformation,
  title={The transformation of gender in English-language fiction},
  author={Underwood, Ted and Bamman, David and Lee, Sabrina},
  journal={Journal of Cultural Analytics},
  volume={3},
  number={2},
  pages={11035},
  year={2018},
  publisher={Department of Languages, Literatures, and Cultures},
    doi = {10.22148/16.019}
}


@article{harrison_re-reading_2018,
	title = {Re-reading in stylistics},
	volume = {27},
	issn = {0963-9470},
	url = {https://doi.org/10.1177/0963947018792719},
	doi = {10.1177/0963947018792719},
	abstract = {Cognitive stylistics is primarily concerned with the cognitive processes ? mental simulations ? experienced by readers. Most cognitive stylisticians agree that experiences of reading texts are dynamic and flexible. Changes in the context of reading, our attentional focus on a given day, our extra background knowledge about the text, and so on, are all factors that contribute to our experience of a fictional world. A second reading of a text is a different experience to a first reading.As researchers begin to systematically distinguish between the ?solitary? and ?social? readings that constitute reading as a phenomenon (Peplow et al., 2016), the relationship between multiple readings and the nature of their processing becomes increasingly pertinent. In order to explore this relationship, firstly we examine the different ways in which re-reading has previously been discussed in stylistics, grounding our claims in an empirical analysis of articles published in key stylistics journals over the past two decades. Next, we draw on reader response data from an online questionnaire in order to assess the role of re-reading and the motivations that underpin it. Finally, we describe an exercise for the teaching of cognitive stylistics, specifically applying schema theory in literary linguistic analysis (Cook, 1994), which illustrates the need to distinguish between readings as part of an analysis. Through these three sections we argue that our experiences of texts should be considered diachronically, and propose that the different readings that make up an analysis of a text should be given greater attention in stylistic research and teaching.},
	number = {3},
	urldate = {2023-04-15},
	journal = {Language and Literature},
	author = {Harrison, Chloe and Nuttall, Louise},
	month = aug,
	year = {2018},
	note = {Publisher: SAGE Publications Ltd},
	pages = {176--195},
	file = {Accepted Version:/Users/au324704/Zotero/storage/BLXY85M9/Harrison and Nuttall - 2018 - Re-reading in stylistics.pdf:application/pdf},
}

% Use this file for citations not found in the ACL Anthology (contained in "anthology.bib").


@article{moore_case_1964,
	title = {The case for poetic obscurity},
	volume = {48},
	issn = {0028-2677, 1572-8668},
	url = {http://link.springer.com/10.1007/BF01515556},
	doi = {10.1007/BF01515556},
	language = {en},
	number = {1},
	urldate = {2023-02-07},
	journal = {Neophilologus},
	author = {Moore, Arthur K.},
	month = dec,
	year = {1964},
	pages = {322--340},
}


@article{ribeiro_sentibench_2016,
	title = {{SentiBench} - a benchmark comparison of state-of-the-practice sentiment analysis methods},
	volume = {5},
	copyright = {2016 Ribeiro et al.},
	issn = {2193-1127},
	doi = {10.1140/epjds/s13688-016-0085-1},
	abstract = {In the last few years thousands of scientific papers have investigated sentiment analysis, several startups that measure opinions on real data have emerged and a number of innovative products related to this theme have been developed. There are multiple methods for measuring sentiments, including lexical-based and supervised machine learning methods. Despite the vast interest on the theme and wide popularity of some methods, it is unclear which one is better for identifying the polarity (i.e., positive or negative) of a message. Accordingly, there is a strong need to conduct a thorough apple-to-apple comparison of sentiment analysis methods, as they are used in practice, across multiple datasets originated from different data sources. Such a comparison is key for understanding the potential limitations, advantages, and disadvantages of popular methods. This article aims at filling this gap by presenting a benchmark comparison of twenty-four popular sentiment analysis methods (which we call the state-of-the-practice methods). Our evaluation is based on a benchmark of eighteen labeled datasets, covering messages posted on social networks, movie and product reviews, as well as opinions and comments in news articles. Our results highlight the extent to which the prediction performance of these methods varies considerably across datasets. Aiming at boosting the development of this research area, we open the methods’ codes and datasets used in this article, deploying them in a benchmark system, which provides an open API for accessing and comparing sentence-level sentiment analysis methods.},
	language = {en},
	number = {1},
	urldate = {2023-03-18},
	journal = {EPJ Data Science},
	author = {Ribeiro, Filipe N. and Araújo, Matheus and Gonçalves, Pollyanna and André Gonçalves, Marcos and Benevenuto, Fabrício},
	month = dec,
	year = {2016},
	pages = {1--29}
}


@book{white_uses_1981,
	title = {The {Uses} of {Obscurity}: {The} {Fiction} of {Early} {Modernism}},
	isbn = {978-0-7100-0751-3},
	shorttitle = {The {Uses} of {Obscurity}},
	language = {en},
	publisher = {Routledge \& Kegan Paul},
	author = {White, Allon and White, Lecturer in English Allon and Allon, White},
	year = {1981}
}

@book{glissant_poetics_1997,
	address = {Ann Arbor},
	title = {Poetics of relation},
	isbn = {978-0-472-09629-9 978-0-472-06629-2},
	publisher = {University of Michigan Press},
	author = {Glissant, Édouard},
	year = {1997}
}


@misc{berlatsky_readability_2015,
	title = {Readability is a myth},
	url = {https://www.theatlantic.com/entertainment/archive/2015/01/readability-is-a-myth/384113/},
	abstract = {There aren't "difficult" books and "easy" ones. There are books that are difficult for some people, and easy for others.},
	language = {en},
	urldate = {2023-02-08},
	journal = {The Atlantic},
	author = {Berlatsky, Noah},
	month = jan,
	year = {2015},
	note = {Section: Culture}
}


@article{shannon_mathematical_1948,
	title = {A {Mathematical} {Theory} of {Communication}},
	volume = {27},
	issn = {00058580},
	url = {https://ieeexplore.ieee.org/document/6773024},
	doi = {10.1002/j.1538-7305.1948.tb01338.x},
	language = {en},
	number = {3},
	urldate = {2023-01-17},
	journal = {Bell System Technical Journal},
	author = {Shannon, C. E.},
	month = jul,
	year = {1948},
	pages = {379--423},
}


@article{delgado-bonal_approximate_2019,
	title = {Approximate {Entropy} and {Sample} {Entropy}: {A} {Comprehensive} {Tutorial}},
	volume = {21},
	issn = {1099-4300},
	shorttitle = {Approximate {Entropy} and {Sample} {Entropy}},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515030/},
	doi = {10.3390/e21060541},
	number = {6},
	urldate = {2022-12-18},
	journal = {Entropy},
	author = {Delgado-Bonal, Alfonso and Marshak, Alexander},
	month = may,
	year = {2019},
	pmid = {33267255},
	pmcid = {PMC7515030},
	pages = {541}
}

@article{DegaetanoOrtliebTeich+2022+175+207,
url = {https://doi.org/10.1515/cllt-2018-0088},
title = {Toward an optimal code for communication: The case of scientific English},
author = {Stefania Degaetano-Ortlieb and Elke Teich},
pages = {175--207},
volume = {18},
number = {1},
journal = {Corpus Linguistics and Linguistic Theory},
doi = {doi:10.1515/cllt-2018-0088},
year = {2022},
lastchecked = {2023-02-09}
}

@misc{AlgeeHewittAllisonGemmaetal.2018,
  author    = {Mark Algee-Hewitt and Sarah Allison and Marissa Gemma and Ryan Heuser and Franco Moretti and Hannah Walser},
  title     = {Canon/archive : large-scale dynamics in the literary field},
  booktitle = {Stanford Literary Lab: Pamphlets ; 11},
  volume    = {Stanford Literary Lab},
  issn      = {2164-1757},
  url       = {https://litlab.stanford.edu/LiteraryLabPamphlet11.pdf},
  series    = {Pamphlets of the Stanford Literary Lab},
  number    = {11},
  type      = {workingpaper},
  pages     = {14},
  year        = {2018},
}

@article{crocker_information_2016,
	title = {Information {Density} and {Linguistic} {Encoding} ({IDeaL})},
	volume = {30},
	issn = {0933-1875, 1610-1987},
	url = {http://link.springer.com/10.1007/s13218-015-0391-y},
	doi = {10.1007/s13218-015-0391-y},
	language = {en},
	number = {1},
	urldate = {2023-02-09},
	journal = {KI - Künstliche Intelligenz},
	author = {Crocker, Matthew W. and Demberg, Vera and Teich, Elke},
	month = feb,
	year = {2016},
	pages = {77--81},
	file = {Full Text:/Users/pascale/Zotero/storage/7YCRYJAA/Crocker et al. - 2016 - Information Density and Linguistic Encoding (IDeaL.pdf:application/pdf},
}


@article{mcgrath_measuring_2018,
	title = {Measuring {Modernist} {Novelty}},
	volume = {3},
	url = {https://culturalanalytics.org/article/11030},
	doi = {10.22148/16.027},
	number = {1},
	urldate = {2023-11-22},
	journal = {Journal of Cultural Analytics},
	author = {McGrath, Laura and Higgins, Devin and Hintze, Arend},
	year = {2018},
	file = {11030-measuring-modernist-novelty.pdf:/Users/au324704/Zotero/storage/X4M6HBP5/11030-measuring-modernist-novelty.pdf:application/pdf;Full Text:/Users/au324704/Zotero/storage/DQIXH2E3/McGrath et al. - 2018 - Measuring Modernist Novelty.pdf:application/pdf},
}

@inproceedings{bizzoni_hc_andersen,
    title = "Sentiment Dynamics of Success: Fractal Scaling of Story Arcs Predicts Reader Preferences",
    author = "Bizzoni, Yuri  and
      Peura, Telma  and
      Thomsen, Mads Rosendahl  and
      Nielbo, Kristoffer",
    booktitle = "Proceedings of the Workshop on Natural Language Processing for Digital Humanities",
    month = dec,
    year = "2021",
    address = "NIT Silchar, India",
    publisher = "NLP Association of India (NLPAI)",
    url = "https://aclanthology.org/2021.nlp4dh-1.1",
    pages = "1--6",
    abstract = "e explore the correlation between the sentiment arcs of H. C. Andersen{'}s fairy tales and their popularity, measured as their average score on the platform GoodReads. Specifically, we do not conceive a story{'}s overall sentimental trend as predictive \textit{per se}, but we focus on its coherence and predictability over time as represented by the arc{'}s Hurst exponent. We find that degrading Hurst values tend to imply degrading quality scores, while a Hurst exponent between .55 and .65 might indicate a {``}sweet spot{''} for literary appreciation.",
}

@article{benedetto_language_2002,
	title = {Language {Trees} and {Zipping}},
	volume = {88},
	issn = {0031-9007, 1079-7114},
	url = {https://link.aps.org/doi/10.1103/PhysRevLett.88.048702},
	doi = {10.1103/PhysRevLett.88.048702},
	language = {en},
	number = {4},
	urldate = {2023-01-17},
	journal = {Physical Review Letters},
	author = {Benedetto, Dario and Caglioti, Emanuele and Loreto, Vittorio},
	month = jan,
	year = {2002},
	pages = {1-5}
}

@article{TORRUELLA2013,
title = {Lexical Statistics and Tipological Structures: A Measure of Lexical Richness},
journal = {Procedia - Social and Behavioral Sciences},
volume = {95},
pages = {447-454},
year = {2013},
issn = {1877-0428},
doi = {https://doi.org/10.1016/j.sbspro.2013.10.668},
url = {https://www.sciencedirect.com/science/article/pii/S1877042813041888},
author = {Joan Torruella and Ramon Capsada},
keywords = {lexicometry, lexical richness, text types},
abstract = {For some time now research has been carried out in the field of lexicometry into the statistical indices that enable lexical richness to be evaluated. The main problem lies in the fact that there should be no influence at all in the results of the formula of the length of the text in terms of the number of words it contains. Therefore, different indices have been designed, which are increasingly complex and sophisticated. This work is a review of the most important indices for calculating lexical richness, in order of complexity, looking into whether or not they are dependent on text length and a comparative analysis of the results of the different indices for different text types is presented.}
}


@inproceedings{lassen_persistence_2023,
	address = {Paris, France},
	title = {Persistence of {Gender} {Asymmetries} in {Book} {Reviews} {Within} and {Across} {Genres}},
	abstract = {While literary judgments are considered highly subjective or noisy, gender asymmetries are shown in readers’ and reviewers’ assessments of literature. This study adds genre categories to the examination of literary reviews. By considering both the gender of authors and reviewers, the media type of reviews (newspapers, online blogs), as well as genres, this analysis provides a detailed overview of the Danish review scene and sheds light on structural biases. Analyzing how genres are reviewed in newspapers and blogs, we identify systematic trends that may be attributed to gender biases in reviewer judgments across, as well as within, di昀昀erent genres and media types. Our results show that the book reviews in Danish media are polarized between the reviewer genders and the two considered media types, upholding additional gender asymmetries in rating.},
	language = {en},
	booktitle = {{CEUR} {Workshop} {Proceedings}},
	author = {Lassen, Ida Marie S and Moreira, Pascale Feldkamp and Bizzoni, Yuri and Thomsen, Mads Rosendahl and Nielbo, Kristoffer L.},
	year = {2023},
	pages = {14--28},
	file = {Lassen et al. - Persistence of Gender Asymmetries in Book Reviews .pdf:/Users/au324704/Zotero/storage/Z3WP6RF6/Lassen et al. - Persistence of Gender Asymmetries in Book Reviews .pdf:application/pdf},
}


@incollection{shklovsky_art_1917,
	title = {Art as technique},
	url = {https://warwick.ac.uk/fac/arts/english/currentstudents/undergraduate/modules/fulllist/first/en122/lecturelist-2015-16-2/shklovsky.pdf},
	urldate = {2023-08-24},
	booktitle = {Literary {Theory}: {An} {Anthology}},
	publisher = {Blackwell Publishing Ltd},
	author = {Shklovsky, Viktor},
	editor = {Rivkin, J and Ryan, M},
	year = {1917},
	pages = {15--21},
	file = {shklovsky.pdf:/Users/au324704/Zotero/storage/S6MYR2NF/shklovsky.pdf:application/pdf},
}

@misc{pope_we_2019,
	title = {We Need to Talk bout the Canon: Demographics in ‘{The} {Norton} {Anthology}'},
	shorttitle = {We Need to Talk bout the Canon},
	url = {https://themillions.com/2019/04/we-need-to-talk-about-canons-picturing-writerly-demographics-in-the-norton-anthology-of-american-literature.html},
	journal = {The Millions},
	author = {Pope, Colin},
	year = {2019},
}


@article{alter_will_2022,
	chapter = {Books},
	title = {Will the {Biggest} {Publisher} in the {United} {States} {Get} {Even} {Bigger}?},
	issn = {0362-4331},
	url = {https://www.nytimes.com/2022/07/31/books/penguin-random-house-simon-schuster-antitrust-trial.html},
	abstract = {The Biden administration is suing to block Penguin Random House from buying Simon \& Schuster. A United States District Court will decide if the sale can proceed.},
	language = {en-US},
	urldate = {2023-06-07},
	journal = {The New York Times},
	author = {Alter, Alexandra and Harris, Elizabeth A. and McCabe, David},
	month = jul,
	year = {2022},
	keywords = {Antitrust Laws and Competition Issues, Bertelsmann AG, Book Trade and Publishing, Books and Literature, Justice Department, Mergers, Acquisitions and Divestitures, Penguin Random House, Simon \& Schuster Inc},
	file = {Snapshot:/Users/au324704/Zotero/storage/5YJFTYZE/penguin-random-house-simon-schuster-antitrust-trial.html:text/html},
}


@article{ragen_uncanonical_1992,
	title = {An Uncanonical Classic: The Politics of the "{Norton} {Anthology}"},
	volume = {41},
	shorttitle = {An {Uncanonical} {Classic}},
	url = {https://www.jstor.org/stable/44312103},
	number = {4},
	journal = {Christianity and Literature},
	author = {Ragen, Brian Abel},
	year = {1992},
	pages = {471--479},
}
@book{peer_quality_2008,
	series = {Linguistic approaches to literature},
	title = {The quality of literature: linguistic studies in literary evaluation},
	isbn = {978-90-272-3336-3},
	shorttitle = {The quality of literature},
	number = {v. 4},
	publisher = {John Benjamins Publishing},
	editor = {Peer, Willie van},
	year = {2008},
	keywords = {Criticism, History and criticism, Literature, Discourse analysis, Literary},
}


@incollection{mukarovsky_standard_1964,
	title = {Standard language and Poetic Language},
	url = {https://monoskop.org/images/3/3a/A_Prague_School_Reader_on_Esthetics_Literary_Structure_and_Style_1964.pdf},
	urldate = {2022-05-02},
	booktitle = {A {Prague} {School} {Reader} on {Esthetics} {Literary} {Structure}, and {Style}},
	publisher = {1932. Georgetown University Press},
	author = {Mukařovský, Jan},
	editor = {Garvin, Paul L.},
	year = {1964},
	pages = {17--30},
	file = {A_Prague_School_Reader_on_Esthetics_Literary_Structure_and_Style_1964.pdf:/Users/au324704/Zotero/storage/BM66AGJJ/A_Prague_School_Reader_on_Esthetics_Literary_Structure_and_Style_1964.pdf:application/pdf},
}


@book{attridge_singularity_2004,
	address = {London; New York},
	title = {The {Singularity} of {Literature}},
	isbn = {978-0-203-42044-7},
	shorttitle = {Singularity},
	language = {English},
	publisher = {Routledge},
	author = {Attridge, Derek},
	year = {2004},
	annote = {
“the distinctiveness of literary inventiveness and singularity: other kinds of inventive text—scientific, philosophical, theological, and so oncan be translated without loss of their singular inventiveness” (Attridge, 2004, p. 73)
“experience of increased possibilities for thought and feeling” (Attridge, 2004, p. 76)

},
	file = {Attridge "Introductory" The Singularity of Literature:/Users/au324704/Zotero/storage/L2U7IMR2/Attridge Introductory The Singularity of Literature.pdf:application/pdf;Attridge "Notes":/Users/au324704/Zotero/storage/QBFZG7SF/Attridge Notes.pdf:application/pdf;Attridge "Singularity" The Singularity of Literature:/Users/au324704/Zotero/storage/ALWCY6D5/Attridge Singularity The Singularity of Literature.pdf:application/pdf},
}


@inproceedings{bizzoni_predicting_2022,
	address = {Marseille, France},
	title = {Predicting {Literary} {Quality} {How} {Perspectivist} {Should} {We} {Be}?},
	url = {https://aclanthology.org/2022.nlperspectives-1.3},
	abstract = {Approaches in literary quality tend to belong to two main grounds: one sees quality as completely subjective, relying on the idiosyncratic nature of individual perspectives on the apperception of beauty; the other is ground-truth inspired, and attempts to find one or two values that predict something like an objective quality: the number of copies sold, for example, or the winning of a prestigious prize. While the first school usually does not try to predict quality at all, the second relies on a single majority vote in one form or another. In this article we discuss the advantages and limitations of these schools of thought and describe a different approach to reader's quality judgments, which moves away from raw majority vote, but does try to create intermediate classes or groups of annotators. Drawing on previous works we describe the benefits and drawbacks of building similar annotation classes. Finally we share early results from a large corpus of literary reviews for an insight into which classes of readers might make most sense when dealing with the appreciation of literary quality.},
	urldate = {2022-11-24},
	booktitle = {Proceedings of the 1st {Workshop} on {Perspectivist} {Approaches} to {NLP} @{LREC2022}},
	publisher = {European Language Resources Association},
	author = {Bizzoni, Yuri and Lassen, Ida Marie and Peura, Telma and Thomsen, Mads Rosendahl and Nielbo, Kristoffer},
	year = {2022},
	pages = {20--25},
	file = {Full Text PDF:/Users/au324704/Zotero/storage/MNNMNVPB/Bizzoni et al. - 2022 - Predicting Literary Quality How Perspectivist Shou.pdf:application/pdf},
}


@article{bizzoni_fractality_2023,
	title = {The {Fractality} of {Sentiment} {Arcs} for {Literary} {Quality} {Assessment}: the {Case} of {Nobel} {Laureates}},
	volume = {NLP4DH},
	issn = {2416-5999},
	shorttitle = {The {Fractality} of {Sentiment} {Arcs} for {Literary} {Quality} {Assessment}},
	url = {https://jdmdh.episciences.org/11406},
	doi = {10.46298/jdmdh.11406},
	abstract = {In the few works that have used NLP to study literary quality, sentiment and emotion analysis have often been considered valuable sources of information. At the same time, the idea that the nature and polarity of the sentiments expressed by a novel might have something to do with its perceived quality seems limited at best. In this paper, we argue that the fractality of narratives, specifically the longterm memory of their sentiment arcs, rather than their simple shape or average valence, might play an important role in the perception of literary quality by a human audience. In particular, we argue that such measure can help distinguish Nobel-winning writers from control groups in a recent corpus of English language novels. To test this hypothesis, we present the results from two studies: (i) a probability distribution test, where we compute the probability of seeing a title from a Nobel laureate at different levels of arc fractality; (ii) a classification test, where we use several machine learning algorithms to measure the predictive power of both sentiment arcs and their fractality measure. Lastly, we perform another experiment to examine whether arc fractality may be used to distinguish more or less popular works within the Nobel canon itself, looking at the probability of higher GoodReads’ ratings at different levels of arc fractality. Our findings seem to indicate that despite the competitive and complex nature of the task, the populations of Nobel and non-Nobel laureates seem to behave differently and can to some extent be told apart by a classifier. Moreover, the probability of Nobel titles having better ratings appears higher at different levels of arc fractality.},
	journal = {Journal of Data Mining \& Digital Humanities},
	author = {Bizzoni, Yuri and Moreira, Pascale Feldkamp and Thomsen, Mads Rosendahl and Nielbo, Kristoffer L.},
	year = {2023},
	pages = {11406},
}


@article {wayne_et_al_readability_novels_vs_newspapers,
    author = {Wayne A. Danielson and Dominic L. Lasorsa and Dae S. Im},
    title ={Journalists and Novelists: A Study of Diverging Styles},
    journal = {Journalism Quarterly},
    volume = {69},
    number = {2},
    pages = {436-446},
    year = {1992}
}

@article {kimball_penny_dreadfulls,
    article_type = {journal},
    title = {Sweeney Todd’s Dreadfuls and Mass Readership},
    author = {Kimball, Courtney},
    journal = {The Journal of Publishing Culture},
    volume = 7,
    year = 2017,
    pages = {1-12}
    }

@article{klancher_mass_readership,
 ISSN = {00138304, 10806547},
 URL = {http://www.jstor.org/stable/2872772},
 author = {Jon P. Klancher},
 journal = {ELH},
 number = {1},
 pages = {155--173},
 publisher = {Johns Hopkins University Press},
 title = {From "Crowd" To "Audience": The Making of an English Mass Readership in the Nineteenth Century},
 urldate = {2023-02-07},
 volume = {50},
 year = {1983}
}

@book{westin2002language,
  title={Language Change in English Newspaper Editorials},
  author={Westin, I.},
  isbn={9789042008632},
  series={Language and computers : studies in practical linguistics},
  url={https://books.google.dk/books?id=k23fX9d2AlgC},
  year={2002},
  publisher={Rodopi}
}
    
@article {readability_scientific_texts,
article_type = {journal},
title = {Research: The readability of scientific texts is decreasing over time},
author = {Plavén-Sigray, Pontus and Matheson, Granville James and Schiffler, Björn Christian and Thompson, William Hedley},
editor = {King, Stuart},
volume = 6,
year = 2017,
month = {sep},
pub_date = {2017-09-05},
pages = {e27725},
citation = {eLife 2017;6:e27725},
doi = {10.7554/eLife.27725},
url = {https://doi.org/10.7554/eLife.27725},
abstract = {Clarity and accuracy of reporting are fundamental to the scientific process. Readability formulas can estimate how difficult a text is to read. Here, in a corpus consisting of 709,577 abstracts published between 1881 and 2015 from 123 scientific journals, we show that the readability of science is steadily decreasing. Our analyses show that this trend is indicative of a growing use of general scientific jargon. These results are concerning for scientists and for the wider public, as they impact both the reproducibility and accessibility of research findings.},
keywords = {metascience, readability, data analysis, jargon, scientific communication},
journal = {eLife},
issn = {2050-084X},
publisher = {eLife Sciences Publications, Ltd},
}

@inproceedings{van-cranenburgh-bod-2017-data,
    title = "A Data-Oriented Model of Literary Language",
    author = "van Cranenburgh, Andreas  and
      Bod, Rens",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/E17-1115",
    pages = "1228--1238",
    abstract = "We consider the task of predicting how literary a text is, with a gold standard from human ratings. Aside from a standard bigram baseline, we apply rich syntactic tree fragments, mined from the training set, and a series of hand-picked features. Our model is the first to distinguish degrees of highly and less literary novels using a variety of lexical and syntactic features, and explains 76.0 {\%} of the variation in literary ratings.",
}

@inproceedings{10.1145/2484712.2484720, author = {Crosbie, Tess and French, Tim and Conrad, Marc}, title = {Towards a Model for Replicating Aesthetic Literary Appreciation}, year = {2013}, isbn = {9781450321945}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/2484712.2484720}, doi = {10.1145/2484712.2484720}, abstract = {This study aims to bridge the gap between subjective literary criticism and natural language processing by creating a model that emulates the results of a survey into literary tastes. A panel of human experts qualified segments of literary text according to how aesthetically pleasing they found them. These segments were then rated for literariness in an open survey using a Likert scale. Each segment was subjected to a parts-of-speech tagger using NLTK and the results compared with those of the survey. Using a Grounded Theory approach, experiments using various combinations of parts-of-speech were carried out in order to build a model that could replicate the results shown in the open survey. The success of this approach confirms the feasibility of using this method to create a more accurate and analytical model of literary criticism involving deeper stylistic markers.}, booktitle = {Proceedings of the Fifth Workshop on Semantic Web Information Management}, articleno = {8}, numpages = {4}, keywords = {literary modeler, NLP, stylistics}, location = {New York, New York}, series = {SWIM '13} }



@article{Winter_2022_hilo,
author = {Marna K. Winter and Kristen O’Neill},
title = {An Exploration of Prevalence and Usage of Hi-Lo Texts in Today’s Classrooms},
journal = {Reading \& Writing Quarterly},
volume = {0},
number = {0},
pages = {1-15},
year  = {2022},
publisher = {Routledge},
doi = {10.1080/10573569.2022.2136589}
}

@book{underwood2019distant,
  title={Distant Horizons: Digital Evidence and Literary Change},
  author={Underwood, T.},
  isbn={9780226612836},
  lccn={2018036446},
  url={https://books.google.dk/books?id=fQo5uwEACAAJ},
  year={2019},
  publisher={University of Chicago Press}
}


@article{underwood_longue_2016,
	title = {The \textit{{Longue} {Durée}} of {Literary} {Prestige}},
	volume = {77},
	issn = {0026-7929, 1527-1943},
	url = {https://read.dukeupress.edu/modern-language-quarterly/article/77/3/321-344/47316},
	doi = {10.1215/00267929-3570634},
	language = {en},
	number = {3},
	urldate = {2023-02-07},
	journal = {Modern Language Quarterly},
	author = {Underwood, Ted and Sellers, Jordan},
	month = sep,
	year = {2016},
	pages = {321--344}
}


@article{moretti_slaugtherhouse_2000,
	title = {The slaugtherhouse of literature},
	volume = {61},
	number = {1},
	journal = {MLQ: Modern Language Quarterly},
	author = {Moretti, Franco},
	year = {2000},
	pages = {207--227},
}

@book{saily_exploring_2017,
	address = {Amsterdam},
	series = {Advances in {Historical} {Sociolinguistics}},
	title = {Exploring {Future} {Paths} for {Historical} {Sociolinguistics}},
	volume = {7},
	isbn = {978-90-272-0086-0 978-90-272-6481-7},
	url = {http://www.jbe-platform.com/content/books/9789027264817},
	language = {en},
	urldate = {2023-02-07},
	publisher = {John Benjamins Publishing Company},
	editor = {Säily, Tanja and Nurmi, Arja and Palander-Collin, Minna and Auer, Anita},
	month = dec,
	year = {2017},
	doi = {10.1075/ahs.7}
}


@book{herbert_history_1894,
	title = {The history of the {English} paragraph.},
	language = {en},
	publisher = {University of Chicago Press},
	author = {Herbert, Edwin},
	year = {1894}
}

@article{liddle_could_2019,
	title = {Could {Fiction} {Have} an {Information} {History}? {Statistical} {Probability} and the {Rise} of the {Novel}},
	doi = {10.22148/16.033},
	language = {en},
	journal = {Journal of Cultural Analytics},
	author = {Liddle, Dallas},
	year = {2019},
	pages = {22},
	annote = {kolmogorov complexity and compressibility as the same thing (li and vitányi v.)
thus compression ratio as a measure of kolmogorov complexity

“Ehret and Szmrecsany assessed essays by second-language learners of English at various degrees of mastery, finding that “essays by more advanced learners tend to be more Kolmogorov complex than essays by less advanced learners.”” (Liddle, 2019, p. 7)
},
	file = {Liddle - Could Fiction Have an Information History Statist.pdf:/Users/pascale/Zotero/storage/CNJMV9RD/Liddle - Could Fiction Have an Information History Statist.pdf:application/pdf},
}

@incollection{whitt_variation_2018,
	address = {Amsterdam},
	title = {Variation of sentence length across time and genre: {Influence} on syntactic usage in {English}},
	volume = {85},
	isbn = {978-90-272-0148-5 978-90-272-6350-6},
	shorttitle = {Variation of sentence length across time and genre},
	url = {https://benjamins.com/catalog/scl.85.10rud},
	language = {en},
	urldate = {2022-11-23},
	booktitle = {Studies in {Corpus} {Linguistics}},
	publisher = {John Benjamins Publishing Company},
	author = {Rudnicka, Karolina},
	editor = {Whitt, Richard J.},
	month = oct,
	year = {2018},
	doi = {10.1075/scl.85.10rud},
	pages = {219--240},
	file = {Rudnicka - 2018 - Variation of sentence length across time and genre.pdf:/Users/pascale/Zotero/storage/MACCW95S/Rudnicka - 2018 - Variation of sentence length across time and genre.pdf:application/pdf},
}

@article{zedelius_beyond_2019,
	title = {Beyond subjective judgments: {Predicting} evaluations of creative writing from computational linguistic features},
	volume = {51},
	issn = {1554-3528},
	shorttitle = {Beyond subjective judgments},
	url = {http://link.springer.com/10.3758/s13428-018-1137-1},
	doi = {10.3758/s13428-018-1137-1},
	language = {en},
	number = {2},
	urldate = {2022-11-26},
	journal = {Behavior Research Methods},
	author = {Zedelius, Claire M. and Mills, Caitlin and Schooler, Jonathan W.},
	month = apr,
	year = {2019},
	pages = {879--894},
	file = {Zedelius et al. - 2019 - Beyond subjective judgments Predicting evaluation.pdf:/Users/pascale/Zotero/storage/ZEDKY9YJ/Zedelius et al. - 2019 - Beyond subjective judgments Predicting evaluation.pdf:application/pdf},
}

@article{dale_formula_1948,
	title = {A {Formula} for {Predicting} {Readability}},
	volume = {27},
	issn = {1555-4023},
	url = {https://www.jstor.org/stable/1473169},
	number = {1},
	urldate = {2023-01-26},
	journal = {Educational Research Bulletin},
	author = {Dale, Edgar and Chall, Jeanne S.},
	year = {1948},
	note = {Publisher: Taylor \& Francis, Ltd.},
	pages = {11--28},
	file = {JSTOR Full Text PDF:/Users/pascale/Zotero/storage/MBY45WGL/Dale and Chall - 1948 - A Formula for Predicting Readability.pdf:application/pdf},
}

@incollection{crestani_analyzing_2019,
	address = {Cham},
	title = {Analyzing the {Adequacy} of {Readability} {Indicators} to a {Non}-{English} {Language}},
	volume = {11696},
	isbn = {978-3-030-28576-0 978-3-030-28577-7},
	url = {http://link.springer.com/10.1007/978-3-030-28577-7_10},
	abstract = {Readability is a linguistic feature that indicates how diﬃcult it is to read a text. Traditional readability formulas were made for the English language. This study evaluates their adequacy to the Portuguese language. We applied the traditional formulas in 10 parallel corpora. We veriﬁed that the Portuguese language had higher grade scores (less readability) in the formulas that use the number of syllables per words or number of complex words per sentence. Formulas that use letters by words instead of syllables by words output similar grade scores. Considering this, we evaluated the correlation of the complex words in 65 Portuguese school books of 12 schooling years. We found out that the concept of complex word as a word with 4 or more syllables, instead of 3 or more syllables as originally used in traditional formulas applied to English texts, is more correlated with the grade of Portuguese school books. In the end, for each traditional readability formula, we adapted it to the Portuguese language performing a multiple linear regression in the same dataset of school books.},
	language = {en},
	urldate = {2023-01-27},
	booktitle = {Experimental {IR} {Meets} {Multilinguality}, {Multimodality}, and {Interaction}},
	publisher = {Springer International Publishing},
	author = {Antunes, Hélder and Lopes, Carla Teixeira},
	editor = {Crestani, Fabio and Braschler, Martin and Savoy, Jacques and Rauber, Andreas and Müller, Henning and Losada, David E. and Heinatz Bürki, Gundula and Cappellato, Linda and Ferro, Nicola},
	year = {2019},
	doi = {10.1007/978-3-030-28577-7_10},
	note = {Series Title: Lecture Notes in Computer Science},
	pages = {149--155},
	file = {Antunes and Lopes - 2019 - Analyzing the Adequacy of Readability Indicators t.pdf:/Users/pascale/Zotero/storage/ASJCRDZZ/Antunes and Lopes - 2019 - Analyzing the Adequacy of Readability Indicators t.pdf:application/pdf},
}

@article{baeza-yates_workshop_nodate,
	title = {Workshop {Organizers}/{Organizing} {Committee}},
	language = {en},
	author = {Baeza-Yates, Ricardo and Moreda, Paloma and Rello, Luz and Saggion, Horacio and Specia, Lucia and Fabra, Universitat Pompeu and Fabra, Universitat Pompeu and Fabra, Universitat Pompeu}
}

@article{walsh_goodreads_2021,
	title = {The {Goodreads} “{Classics}”: {A} {Computational} {Study} of {Readers}, {Amazon}, and {Crowdsourced} {Amateur} {Criticism}},
	shorttitle = {The {Goodreads} “{Classics}”},
	url = {https://post45.org/2021/04/the-goodreads-classics-a-computational-study-of-readers-amazon-and-crowdsourced-amateur-criticism/},
	abstract = {What is a classic? This is “not a new question,” as T.S. Eliot acknowledged more than seventy-five years ago.(( T. S. Eliot, What Is a Classic?: An Address Delivered Before the Virgil Soc…},
	language = {en-US},
	urldate = {2022-10-09},
	journal = {Post45: Peer Reviewed},
	author = {Walsh, Melanie and Antoniak, Maria},
	month = apr,
	year = {2021}
}

@article{shesgreen_canonizing,
 ISSN = {00931896, 15397858},
 URL = {https://www.jstor.org/stable/10.1086/596644},
 author = {Sean Shesgreen},
 journal = {Critical Inquiry},
 number = {2},
 pages = {293--318},
 publisher = {The University of Chicago Press},
 title = {Canonizing the Canonizer: A Short History of The Norton Anthology of English Literature},
 urldate = {2023-02-16},
 volume = {35},
 year = {2009}
}


@article{flesch_new_1948,
	title = {A new readability yardstick.},
	volume = {32},
	issn = {1939-1854, 0021-9010},
	url = {http://doi.apa.org/getdoi.cfm?doi=10.1037/h0057532},
	doi = {10.1037/h0057532},
	language = {en},
	number = {3},
	urldate = {2023-01-29},
	journal = {Journal of Applied Psychology},
	author = {Flesch, Rudolph},
	year = {1948},
	pages = {221--233},
	file = {Flesch - 1948 - A new readability yardstick..pdf:/Users/pascale/Zotero/storage/8NEDNJE3/Flesch - 1948 - A new readability yardstick..pdf:application/pdf},
}

@book{kirsch_adult_1993,
	address = {Washington, D.C},
	edition = {2nd ed},
	title = {Adult literacy in {America}: a first look at the results of the {National} {Adult} {Literacy} {Survey}},
	isbn = {978-0-16-041929-4},
	shorttitle = {Adult literacy in {America}},
	publisher = {Office of Educational Research and Improvement, U.S. Dept. of Education},
	editor = {Kirsch, Irwin S. and United States and Educational Testing Service and National Center for Education Statistics},
	year = {1993},
	keywords = {Adult education, Educational surveys, Functional literacy, Literacy, National Adult Literacy Survey, United States}
 }

@article{kouame_using_2010,
	title = {Using {Readability} {Tests} to {Improve} the {Accuracy} of {Evaluation} {Documents} {Intended} for {Low}-{Literate} {Participants}},
	volume = {6},
	language = {en},
	number = {14},
	author = {Kouamé, Julien B},
	year = {2010}
}

@inproceedings{jatowt_longitudinal_2012,
	address = {Washington DC USA},
	title = {Longitudinal analysis of historical texts' readability},
	isbn = {978-1-4503-1154-0},
	url = {https://dl.acm.org/doi/10.1145/2232817.2232887},
	doi = {10.1145/2232817.2232887},
	abstract = {Digital libraries often contain historical documents of varying age. The degree to which users can understand their content depends much on their reading difficulty. In this poster paper we report the results of our studies on the readability of historical documents from the viewpoint of present users. We investigate the correlation between the outcomes of different readability measurements and publication dates of prose texts on the basis of two datasets, the Victorian Women’s Writers Project and the Corpus of Late Modern English Texts.},
	language = {en},
	urldate = {2023-02-02},
	booktitle = {Proceedings of the 12th {ACM}/{IEEE}-{CS} joint conference on {Digital} {Libraries}},
	publisher = {ACM},
	author = {Jatowt, Adam and Tanaka, Katsumi},
	month = jun,
	year = {2012},
	pages = {353--354}
}

@article{plaven-sigray_readability_2017,
	title = {The readability of scientific texts is decreasing over time},
	volume = {6},
	issn = {2050-084X},
	url = {https://elifesciences.org/articles/27725},
	doi = {10.7554/eLife.27725},
	abstract = {Clarity and accuracy of reporting are fundamental to the scientific process. Readability formulas can estimate how difficult a text is to read. Here, in a corpus consisting of 709,577 abstracts published between 1881 and 2015 from 123 scientific journals, we show that the readability of science is steadily decreasing. Our analyses show that this trend is indicative of a growing use of general scientific jargon. These results are concerning for scientists and for the wider public, as they impact both the reproducibility and accessibility of research findings.},
	language = {en},
	urldate = {2023-02-02},
	journal = {eLife},
	author = {Plavén-Sigray, Pontus and Matheson, Granville James and Schiffler, Björn Christian and Thompson, William Hedley},
	month = sep,
	year = {2017},
	pages = {e27725}
}

@article{danielson_journalists_1992,
	title = {Journalists and {Novelists}: {A} {Study} of {Diverging} {Styles}},
	volume = {69},
	issn = {0022-5533},
	shorttitle = {Journalists and {Novelists}},
	url = {http://journals.sagepub.com/doi/10.1177/107769909206900217},
	doi = {10.1177/107769909206900217},
	abstract = {Readability of news stories sampled for each year for more than a century, from 1885 to 1989, from the New York Times and Los Angeles Times reveals a gradual drop in readability, mainly due to journalistic use of longer words but not to the use of longer sentences. A matched sample of sentences from novels published in the same years show an increase in readability, mainly because novelists used even shorter sentences and continued to use simple words.},
	language = {en},
	number = {2},
	urldate = {2023-02-02},
	journal = {Journalism Quarterly},
	author = {Danielson, Wayne A. and Lasorsa, Dominic L. and Im, Dae S.},
	month = jun,
	year = {1992},
	pages = {436--446},
}

@article{danielson_new_1989,
	title = {A {New} {Readability} {Formula} {Based} on the {Stylistic} {Age} of {Novels}},
	volume = {33},
	issn = {0022-4103},
	url = {https://www.jstor.org/stable/40026856},
	number = {3},
	urldate = {2023-02-02},
	journal = {Journal of Reading},
	author = {Danielson, Wayne A. and Lasorsa, Dominic L.},
	year = {1989},
	note = {Publisher: [Wiley, International Reading Association]},
	pages = {194--197}
 }

@book{dubay_principles_2004,
	title = {The {Principles} of {Readability}},
	publisher = {Impact Information},
	author = {Dubay, William},
	year = {2004}
}

@inproceedings{stajner_what_2012,
	address = {Istanbul, Turkey},
	title = {What {Can} {Readability} {Measures} {Really} {Tell} {Us} {About} {Text} {Complexity}?},
	url = {http://nlx-server.di.fc.ul.pt/~sanja/StajnerEtAl-12.pdf},
	urldate = {2023-01-27},
	booktitle = {Proceedings of {Workshop} on natural language processing for improving textual accessibility},
	publisher = {Association for Computational Linguistics},
	author = {Stajner, Sanja and Evans, Richard and Orasan, Constantin and Mitkov, Ruslan},
	year = {2012},
	pages = {14--22}
 }

@article{dalvean_new_2018,
	title = {A {New} {Fiction} {Text} {Complexity} {Metric} for {Ranking} {Fiction} {Texts}},
	author = {Dalvean, Michael and Enkhbayar, Galbadrakh},
	year = {2018},
	pages = {1--29}
}

@article{dalvean_assessing_2018,
	title = {Assessing the readability of fiction: a corpus analysis and readability ranking of 200 {English} fiction texts},
	volume = {35},
	shorttitle = {Assessing the readability of fiction},
	url = {https://doi.org/10.17250/KHISLI.35..201809.006},
	doi = {10.17250/KHISLI.35..201809.006},
	urldate = {2023-02-03},
	journal = {Linguistic Research},
	author = {Dalvean, Michael and Enkhbayar, Galbadrakh},
	year = {2018},
	pages = {137--170}
}

@book{Dubay,
author = {Dubay, William},
year = {2004},
month = {01},
pages = {631-3309},
title = {The Principles of Readability},
publisher = {Impact Information}
}

@article{dale_chall_1949,
 ISSN = {00135968},
 URL = {http://www.jstor.org/stable/41383594},
 author = {Edgar Dale and Jeanne S. Chall},
 journal = {Elementary English},
 number = {1},
 pages = {19--26},
 publisher = {National Council of Teachers of English},
 title = {The Concept of Readability},
 urldate = {2023-01-27},
 volume = {26},
 year = {1949}
}

@article{Chall_1947,
 ISSN = {15554023},
 URL = {http://www.jstor.org/stable/1474414},
 author = {Jeanne S. Chall},
 journal = {Educational Research Bulletin},
 number = {1},
 pages = {1--13},
 publisher = {Taylor & Francis, Ltd.},
 title = {This Business of Readability},
 urldate = {2023-01-27},
 volume = {26},
 year = {1947}
}


@article{Flesch_1948,
author = {Flesch, Rudolph},
year = {1948},
pages = {221-233},
title = {A New Readability Yardstick},
volume = {32},
journal = {Journal of Applied Psychology}
}

@article{Dale-chall,
 ISSN = {15554023},
 URL = {http://www.jstor.org/stable/1473169},
 author = {Edgar Dale and Jeanne S. Chall},
 journal = {Educational Research Bulletin},
 number = {1},
 pages = {11--28},
 publisher = {Taylor & Francis, Ltd.},
 title = {A Formula for Predicting Readability},
 urldate = {2023-01-26},
 volume = {27},
 year = {1948}
}

@article{Mchlaughlin,
 author = {Harry G. McLaughlin},
 journal = {Journal of Reading},
 number = {1},
 pages = {639--646},
 publisher = {Taylor & Francis, Ltd.},
 title = {SMOG Grading: A New Readability Formula},
 volume = {12},
 year = {1969}
}

@article{redish_2000_readability_shortcomings, author = {Redish, Janice}, 
title = {Readability Formulas Have Even More Limitations than {K}lare Discusses}, year = {2000}, issue_date = {Aug. 2000}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {24}, number = {3}, issn = {1527-6805}, url = {https://doi.org/10.1145/344599.344637}, doi = {10.1145/344599.344637}, abstract = {A literature review reveals many technical weaknesses of readability formulas (when compared to direct usability testing with typical readers): they were developed for children s school books, not adult technical documentation;they ignore between-reader differences and the effects of content, layout, and retrieval aids on text usefulness; they emphasize countable features at the expense of more subtle contributors to text comprehension.}, journal = {ACM J. Comput. Doc.}, month = {aug}, pages = {132–137}, numpages = {6}, keywords = {reliability, text comprehension, usability testing} }

@article{Garthwaite_2014,
Author = {Garthwaite, Craig L.},
Title = {Demand Spillovers, Combative Advertising, and Celebrity Endorsements},
Journal = {American Economic Journal: Applied Economics},
Volume = {6},
Number = {2},
Year = {2014},
Month = {April},
Pages = {76-104},
DOI = {10.1257/app.6.2.76},
URL = {https://www.aeaweb.org/articles?id=10.1257/app.6.2.76}}

@article{martin_1996,
author = {Martin, Claude},
title = {Production, Content, and Uses of Bestselling Books in Quebec},
journal = {Canadian Journal of Communication},
volume = {21},
number = {4},
pages = {},
year = {1996},
doi = {10.22230/cjc.1996v21n4a958}
}

@book{chall1995readability,
  title={Readability Revisited: The New Dale-Chall Readability Formula},
  author={Chall, Jeanne S. and Dale, Edgar},
  isbn={9781571290120},
  lccn={95016034},
  url={https://books.google.dk/books?id=2nbuAAAAMAAJ},
  year={1995},
  publisher={Brookline Books}
}


@incollection{crestani_analyzing_2019,
	address = {Cham},
	title = {Analyzing the {Adequacy} of {Readability} {Indicators} to a {Non}-{English} {Language}},
	volume = {11696},
	isbn = {978-3-030-28576-0 978-3-030-28577-7},
	booktitle = {Experimental {IR} {Meets} {Multilinguality}, {Multimodality}, and {Interaction}},
	publisher = {Springer International Publishing},
	author = {Antunes, Hélder and Lopes, Carla Teixeira},
	editor = {Crestani, Fabio and Braschler, Martin and Savoy, Jacques and Rauber, Andreas and Müller, Henning and Losada, David E. and Heinatz Bürki, Gundula and Cappellato, Linda and Ferro, Nicola},
	year = {2019},
	doi = {10.1007/978-3-030-28577-7_10},
	pages = {149--155}
}


@article{clark_booker_2011,
	chapter = {Books},
	title = {Man {Booker} Prize: This Year's Judges are Betraying Authors and Their Readers},
	issn = {0029-7712},
	shorttitle = {Man {Booker} prize},
	url = {https://www.theguardian.com/books/2011/oct/16/booker-prize-judges-betray-readers},
	abstract = {Former Booker judge Alex Clark says the panel's says the panel's philistine attacks on literary fiction have devalued their award},
	language = {en-GB},
	urldate = {2023-01-29},
	journal = {The Observer},
	author = {Clark, Alex},
	month = oct,
	year = {2011},
	keywords = {Booker prize 2011, Books},
	file = {Snapshot:/Users/pascale/Zotero/storage/Y2IN2YV2/booker-prize-judges-betray-readers.html:text/html},
}


@article{nakamura_2013, title={“{W}ords with Friends”: Socially Networked Reading on {Goodreads}}, volume={128}, DOI={10.1632/pmla.2013.128.1.238}, number={1}, journal={PMLA}, publisher={Cambridge University Press}, author={Nakamura, Lisa}, year={2013}, pages={238–243}}

@book{sherman1893analytics,
  title={Analytics of Literature: A Manual for the Objective Study of English Prose and Poetry},
  author={Sherman, Lucius A.},
  lccn={12038822},
  series={Athenaeum Press},
  year={1893},
  publisher={Ginn}
}

@inproceedings {ganjigunte2013,
    title = {Success with Style: Using Writing Style to Predict the Success of Novels},
    author = {Ganjigunte Ashok, Vikas  and Feng, Song  and Choi, Yejin},
    booktitle = {Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing},
    year = {2013},
    address = {Seattle, Washington, USA},
    publisher = {Association for Computational Linguistics},
    pages = {1753--1764}
}

@inproceedings{maharjan_multi-task_2017,
	address = {Valencia, Spain},
	title = {A Multi-task Approach to Predict Likability of Books},
	url = {https://aclanthology.org/E17-1114},
	urldate = {2022-12-16},
	booktitle = {Proceedings of the 15th {Conference} of the {European} {Chapter} of the {Association} for {Computational} {Linguistics}: {Volume} 1, {Long} {Papers}},
	publisher = {Association for Computational Linguistics},
	author = {Maharjan, Suraj and Arevalo, John and Montes, Manuel and González, Fabio A. and Solorio, Thamar},
	month = apr,
	year = {2017},
	pages = {1217--1227}
}


@article{brottrager_modeling_2022,
	title = {Modeling and Predicting Literary Reception},
	volume = {1},
	language = {en},
	number = {1},
	journal = {Journal of Computational Literary Studies},
	author = {Brottrager, Judith and Stahl, Annina and Arslan, Arda and Brandes, Ulrik and Weitin, Thomas},
	year = {2022},
	pages = {1--27},
	file = {Brottrager et al. - 2022 - Modeling and Predicting Literary Reception.pdf:/Users/pascale/Zotero/storage/RSF8Q4WD/Brottrager et al. - 2022 - Modeling and Predicting Literary Reception.pdf:application/pdf},
}


@inproceedings{crosbie_2013, author = {Crosbie, Tess and French, Tim and Conrad, Marc}, title = {Towards a Model for Replicating Aesthetic Literary Appreciation}, year = {2013}, isbn = {9781450321945}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/2484712.2484720}, doi = {10.1145/2484712.2484720}, booktitle = {Proceedings of the Fifth Workshop on Semantic Web Information Management}, articleno = {8}, pages = {1-4}, numpages = {4}, keywords = {NLP, literary modeler, stylistics}, location = {New York, New York}, series = {SWIM '13} }


@inproceedings{cranenburgh_data-oriented_2017,
	address = {Valencia, Spain},
	title = {A Data-Oriented Model of Literary Language},
	url = {https://aclanthology.org/E17-1115},
	urldate = {2022-09-15},
	booktitle = {Proceedings of the 15th {Conference} of the {European} {Chapter} of the {Association} for {Computational} {Linguistics}: {Volume} 1, {Long} {Papers}},
	publisher = {Association for Computational Linguistics},
	author = {van Cranenburgh, Andreas and Bod, Rens},
	month = apr,
	year = {2017},
	pages = {1228--1238}
}


@article{riddlers,
	title = {Literary Quality in the Eye of the {Dutch} Reader: The National Reader Survey},
	volume = {79},
	doi = {10.1016/j.poetic.2020.101439},
	language = {en},
	urldate = {2022-09-08},
    pages = {1-13},
	journal = {Poetics},
	author = {Koolen, Corina and van Dalen-Oskam, Karina and Cranenburgh, Andreas van and Nagelhout, Erica},
	year = {2020},
}


@inproceedings{jautze_topic_2016,
	address = {Kraków},
	title = {Topic {Modeling} {Literary} {Quality}},
	language = {en},
	booktitle = {Digital {Humanities} 2016: {Conference} {Abstracts}.},
	author = {Jautze, Kim and van Cranenburgh, Andreas and Koolen, Corina},
	year = {2016},
	pages = {233--237},
	file = {Jautze et al. - Topic Modeling Literary Quality.pdf:/Users/au324704/Zotero/storage/ZW7WFNHG/Jautze et al. - Topic Modeling Literary Quality.pdf:application/pdf},
}


@inproceedings{Maharjan,
    title = "Letting Emotions Flow: Success Prediction by Modeling the Flow of Emotions in Books",
    author = "Maharjan, Suraj  and
      Kar, Sudipta  and
      Montes, Manuel  and
      Gonz{\'a}lez, Fabio A.  and
      Solorio, Thamar",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies: Volume 2, Short Papers",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/N18-2042",
    pages = "259--265",
}

@article{mohseni_approximate_2022,
	title = {Approximate Entropy in Canonical and Non-Canonical Fiction},
	volume = {24},
	issn = {1099-4300},
	url = {https://www.mdpi.com/1099-4300/24/2/278},
	doi = {10.3390/e24020278},
	language = {en},
	number = {2},
	urldate = {2022-09-22},
	journal = {Entropy},
	author = {Mohseni, Mahdi and Redies, Christoph and Gast, Volker},
	month = feb,
	year = {2022},
	pages = {278}
}


@article{wang_success_2019,
	title = {Success in Books: Predicting Book Sales Before Publication},
	volume = {8},
	issn = {2193-1127},
	shorttitle = {Success in books},
	doi = {10.1140/epjds/s13688-019-0208-6},
	language = {en},
	number = {1},
	urldate = {2022-11-23},
	journal = {EPJ Data Science},
	author = {Wang, Xindi and Yucesoy, Burcu and Varol, Onur and Eliassi-Rad, Tina and Barabási, Albert-László},
	month = dec,
	year = {2019},
	pages = {31}
 }

@inproceedings{stajner12,
author = {Stajner, Sanja and Evans, Richard and Orasan, Constantin and Mitkov, Ruslan},
year = {2012},
month = {01},
pages = {14-22},
title = {What Can Readability Measures Really Tell Us About Text Complexity?},
address = {Istanbul, Turkey},
booktitle = {Proceedings of Workshop on natural language processing for improving textual accessibility},
publisher = {Association for Computational Linguistics}
}














@book{Aho:72,
    author  = {Alfred V. Aho and Jeffrey D. Ullman},
    title   = {{The Theory of Parsing, Translation and Compiling}},
    year    = "1972",
    volume  = "1",
    publisher = {Prentice-Hall},
    address = {Englewood Cliffs, NJ}
}

@book{APA:83,
    author  = {{American Psychological Association}},
    title   = {Publications Manual},
    year    = "1983",
   publisher = {American Psychological Association},
   address = {Washington, DC}
}

@article{ACM:83,
	author = {Association for Computing Machinery},
	year = "1983",
	journal = {Computing Reviews},
	volume = "24",
	number = "11",
	pages = "503--512"
}

@article{Chandra:81,
	author = {Ashok K. Chandra and Dexter C. Kozen and Larry J. Stockmeyer},
	year = {1981},
	title = {Alternation},
	journal = {Journal of the Association for Computing Machinery},
	volume = "28",	
        number = "1",
	pages = "114--133",
}

@inproceedings{andrew2007scalable,
  title={Scalable training of {L1}-regularized log-linear models},
  author={Andrew, Galen and Gao, Jianfeng},
  booktitle={Proceedings of the 24th International Conference on Machine Learning},
  pages={33--40},
  year={2007},
}

@article{vaswani2017attention,
  title={Attention is all you need},
  author={Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, {\L}ukasz and Polosukhin, Illia},
  journal={Advances in Neural Information Processing Systems},
  volume={30},
  year={2017}
}

@Book{Gusfield:97,
    author  = {Dan Gusfield},
    year = 1997,
    title   = {{Algorithms on Strings, Trees and Sequences}},
    address = {Cambridge, UK},
    publisher   = {Cambridge University Press}
}


@inproceedings{borsch2011,
	Address = {Canberra, Australia},
	Author = {Benjamin Borschinger and Mark Johnson},
	Booktitle = {Proceedings of the Australasian Language Technology Association Workshop 2011},
	Month = {December},
	Pages = {10--18},
	Title = {A Particle Filter algorithm for {B}ayesian Wordsegmentation},
	Year = {2011}}

@article{rasooli-tetrault-2015,
    author    = {Mohammad Sadegh Rasooli and Joel R. Tetreault},
    title     = {Yara Parser: {A} Fast and Accurate Dependency Parser},
    journal   = {Computing Research Repository},
    volume    = {arXiv:1503.06733},
    year      = {2015},
    url       = {http://arxiv.org/abs/1503.06733},
    note    = {version 2}
}

@article{Ando2005,
	Acmid = {1194905},
	Author = {Ando, Rie Kubota and Zhang, Tong},
	Issn = {1532-4435},
	Issue_Date = {12/1/2005},
	Journal = {Journal of Machine Learning Research},
	Month = dec,
	Numpages = {37},
	Pages = {1817--1853},
	Publisher = {JMLR.org},
	Title = {A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data},
	Volume = {6},
	Year = {2005}}

@InProceedings{P16-1001,
  author =	"Goodman, James
  	 and Vlachos, Andreas
	     and Naradowsky, Jason",
  title =    "Noise reduction and targeted exploration in imitation learning for      Abstract Meaning Representation parsing    ",
  booktitle = 	    "Proceedings of the 54th Annual Meeting of the Association for      Computational Linguistics (Volume 1: Long Papers)    ",
  year =    "2016",
  publisher =	"Association for Computational Linguistics",
  pages =   "1--11",
  location =	"Berlin, Germany",
  doi =    "10.18653/v1/P16-1001",
  url =    "http://aclweb.org/anthology/P16-1001"
}

@InProceedings{C14-1001,
  author =	"Harper, Mary",
  title = 	"Learning from 26 Languages: Program Management and Science in the Babel Program",
  booktitle = 	"Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers",
  year =    "2014",
  publisher =	"Dublin City University and Association for Computational Linguistics",
  pages =   "1",
  location =	"Dublin, Ireland",
  url =    "http://aclweb.org/anthology/C14-1001"
}

@article{pincus91a,
author = {Pincus,Steve},
title = {Approximate entropy (ApEn) as a complexity measure},
journal = {Chaos: An Interdisciplinary Journal of Nonlinear Science},
volume = {5},
number = {1},
pages = {110-117},
year = {1991},
doi = {10.1063/1.166092}
}

@article{pincus91b,
author = {Pincus,Steve and Gladstone,Igor and Ehrenkranz, Richard},
title = {A regularity statistic for medical data analysis},
journal = {Journal of Clinical Monitoring},
volume = {7},
number = {4},
pages = {335-345},
year = {1991},
doi = {https://doi.org/10.1007/BF01619355}
}

@article {Fleisher92,
	Title = {Approximate entropy of heart rate as a correlate of postoperative ventricular dysfunction},
	Author = {Fleisher, Lee and Pincus, Steve and Rosenbaum, Stanley},
	DOI = {10.1097/00000542-199304000-00011},
	Number = {4},
	Volume = {78},
	Month = {April},
	Year = {1993},
	Journal = {Anesthesiology},
	ISSN = {0003-3022},
	Pages = {683—692}
}


@inproceedings{cox_long_1984,
	title = {Long range dependence: {A} review},
	booktitle = {Iowa {State} {University}},
	publisher = {Press},
	author = {Cox, Timothy M.},
	year = {1984}
}

@article{riley_tutorial_2012,
	title = {A tutorial introduction to adaptive fractal analysis},
	volume = {3},
	issn = {1664-042X},
	url = {http://journal.frontiersin.org/article/10.3389/fphys.2012.00371/abstract},
	doi = {10.3389/fphys.2012.00371},
	urldate = {2016-05-19},
	journal = {Frontiers in Physiology},
	author = {Riley, Michael A. and Bonnette, Scott and Kuznetsov, Nikita and Wallot, Sebastian and Gao, Jianbo},
	year = {2012},
	file = {Gao_2012_Adaptive_fractial_analysis.pdf:C\:\\Users\\KLN\\Documents\\gao\\Gao_2012_Adaptive_fractial_analysis.pdf:application/pdf}
}

@book{mandelbrot_fractal_1982,
	address = {San Francisco},
	title = {The {Fractal} {Geometry} of {Nature}},
	isbn = {978-0-7167-1186-5},
	abstract = {Clouds are not spheres, mountains are not cones, and lightening does not travel in a straight line. The complexity of nature's shapes differs in kind, not merely degree, from that of the shapes of ordinary geometry, the geometry of fractal shapes. Now that the field has expanded greatly with many active researchers, Mandelbrot presents the definitive overview of the origins of his ideas and their new applications. The Fractal Geometry of Nature is based on his highly acclaimed earlier work, but has much broader and deeper coverage and more extensive illustrations.},
	language = {English},
	publisher = {Times Books},
	author = {Mandelbrot, Benoit},
	year = {1982}
}

@article{mandelbrot_fractional_1968,
	title = {Fractional {Brownian} {Motions}, {Fractional} {Noises} and {Applications}},
	volume = {10},
	issn = {00361445},
	url = {http://www.jstor.org/stable/2027184},
	number = {4},
	journal = {SIAM Review},
	author = {Mandelbrot, Benoit B. and Ness, John W. Van},
	year = {1968},
	pages = {422--437}
}

@book{beran_statistics_1994,
	address = {New York},
	edition = {1},
	title = {Statistics for {Long}-{Memory} {Processes}},
	isbn = {978-0-412-04901-9},
	abstract = {Statistical Methods for Long Term Memory Processes covers the diverse statistical methods and applications for data with long-range dependence. Presenting material that previously appeared only in journals, the author provides a concise and effective overview of probabilistic foundations, statistical methods, and applications. The material emphasizes basic principles and practical applications and provides an integrated perspective of both theory and practice. This book explores data sets from a wide range of disciplines, such as hydrology, climatology, telecommunications engineering, and high-precision physical measurement. The data sets are conveniently compiled in the index, and this allows readers to view statistical approaches in a practical context. Statistical Methods for Long Term Memory Processes also supplies S-PLUS programs for the major methods discussed. This feature allows the practitioner to apply long memory processes in daily data analysis. For newcomers to the area, the first three chapters provide the basic knowledge necessary for understanding the remainder of the material. To promote selective reading, the author presents the chapters independently. Combining essential methodologies with real-life applications, this outstanding volume is and indispensable reference for statisticians and scientists who analyze data with long-range dependence.},
	language = {English},
	publisher = {Chapman and Hall/CRC},
	author = {Beran, Jan},
	month = oct,
	year = {1994}
}



@book{mandelbrot_fractals_1997,
	address = {New York},
	edition = {1997 edition},
	title = {Fractals and {Scaling} in {Finance}: {Discontinuity}, {Concentration}, {Risk}. {Selecta} {Volume} {E}},
	isbn = {978-0-387-98363-9},
	shorttitle = {Fractals and {Scaling} in {Finance}},
	abstract = {Mandelbrot is world famous for his creation of the new mathematics of fractal geometry. Yet few people know that his original field of applied research was in econometrics and financial models, applying ideas of scaling and self-similarity to arrays of data generated by financial analyses. This book brings together his original papers as well as many original chapters specifically written for this book.},
	language = {English},
	publisher = {Springer},
	author = {Mandelbrot, Benoit B.},
	month = sep,
	year = {1997}
}

@article{kuznetsov_adaptive_2013,
	title = {Adaptive {Fractal} {Analysis} {Reveals} {Limits} to {Fractal} {Scaling} in {Center} of {Pressure} {Trajectories}},
	volume = {41},
	issn = {0090-6964, 1573-9686},
	url = {http://link.springer.com/10.1007/s10439-012-0646-9},
	doi = {10.1007/s10439-012-0646-9},
	abstract = {Fractal time series analysis methods are commonly used for analyzing center of pressure (COP) signals with the goal of revealing the underlying neuromuscular processes for upright stance control. The use of fractal methods is often coupled with the assumption that the COP is an instance of fractional Gaussian noise (fGn) or fractional Brownian motion (fBm). Our purpose was to evaluate the applicability of the fGn–fBm framework to the COP in light of several characteristics of COP signals revealed by a new method, adaptive fractal analysis (AFA). AFA quantiﬁes how the variance of the residuals to ﬁts of a globally smooth trend signal scales with the time scale at which the ﬁts are performed. Application of AFA to COP signals revealed that there are potentially three fractal scaling regions in the COP as opposed to one as expected from a pure fGn or fBm process. The scaling region at the fastest scale was antipersistent and spanned {\textasciitilde}30–90 ms, the intermediate was persistent and spanned {\textasciitilde}200 ms–1.9 s, and the slowest was anti-persistent and spanned {\textasciitilde}5–40 s. The intermediate fractal scaling region was the most clearly deﬁned, but it only contributed around 11\% of the total spectral energy of the COP signal, indicating that other features of the COP signal contribute more importantly to the overall dynamics. Also, more than half of the Hurst exponents estimated for the intermediate region were greater than the theoretically expected range [0,1] for fGn–fBm processes. These results suggest the fGn–fBm framework is not appropriate for modeling COP signals. ON–OFF intermittency might provide a better modeling framework for the COP, and multiscale approaches may be more appropriate for analyzing COP data.},
	language = {en},
	number = {8},
	urldate = {2018-04-02},
	journal = {Annals of Biomedical Engineering},
	author = {Kuznetsov, Nikita and Bonnette, Scott and Gao, Jianbo and Riley, Michael A.},
	month = aug,
	year = {2013},
	pages = {1646--1660},
	file = {Kuznetsov et al. - 2013 - Adaptive Fractal Analysis Reveals Limits to Fracta.pdf:/home/knielbo/Documents/articles/fractals/Kuznetsov et al. - 2013 - Adaptive Fractal Analysis Reveals Limits to Fracta.pdf:application/pdf}
}

@article{eke_fractal_2002,
	title = {Fractal characterization of complexity in temporal physiological signals},
	volume = {23},
	url = {http://stacks.iop.org/0967-3334/23/i=1/a=201},
	abstract = {This review first gives an overview on the concept of fractal geometry with definitions and explanations of the most fundamental properties of fractal structures and processes like self-similarity, power law scaling relationship, scale invariance, scaling range and fractal dimensions. Having laid down the grounds of the basics in terminology and mathematical formalism, the authors systematically introduce the concept and methods of monofractal time series analysis. They argue that fractal time series analysis cannot be done in a conscious, reliable manner without having a model capable of capturing the essential features of physiological signals with regard to their fractal analysis. They advocate the use of a simple, yet adequate, dichotomous model of fractional Gaussian noise (fGn) and fractional Brownian motion (fBm). They demonstrate the importance of incorporating a step of signal classification according to the fGn/fBm model prior to fractal analysis by showing that missing out on signal class can result in completely meaningless fractal estimates. Limitation and precision of various fractal tools are thoroughly described and discussed using results of numerical experiments on ideal monofractal signals. Steps of a reliable fractal analysis are explained. Finally, the main applications of fractal time series analysis in biomedical research are reviewed and critically evaluated.},
	number = {1},
	journal = {Physiological Measurement},
	author = {Eke, A. and Herman, P. and Kocsis, L. and Kozak, L. R.},
	year = {2002},
	pages = {R1},
	file = {eke_physiological_signals_2002.pdf:/home/knielbo/Documents/articles/fractals/eke_physiological_signals_2002.pdf:application/pdf}
}



@article{gao_facilitating_2011,
	title = {Facilitating {Joint} {Chaos} and {Fractal} {Analysis} of {Biosignals} through {Nonlinear} {Adaptive} {Filtering}},
	volume = {6},
	issn = {1932-6203},
	url = {http://dx.plos.org/10.1371/journal.pone.0024331},
	doi = {10.1371/journal.pone.0024331},
	language = {en},
	number = {9},
	urldate = {2017-10-18},
	journal = {PLoS ONE},
	author = {Gao, Jianbo and Hu, Jing and Tung, Wen-wen},
	editor = {Perc, Matjaz},
	month = sep,
	year = {2011},
	pages = {e24331},
	file = {afa_original.PDF:/home/kln/Documents/art/hurst/afa_original.PDF:application/pdf}
}

@article{cannon_evaluating_1997,
	title = {Evaluating scaled windowed variance methods for estimating the {Hurst} coefficient of time series},
	volume = {241},
	number = {3-4},
	journal = {Physica A: Statistical Mechanics and its Applications},
	author = {Cannon, Michael J. and Percival, Donald B. and Caccia, David C. and Raymond, Gary M. and Bassingthwaighte, James B.},
	year = {1997},
	pages = {606--626},
	file = {cannon_windowvariance_1997.pdf:/home/knielbo/Documents/articles/fractals/cannon_windowvariance_1997.pdf:application/pdf}
}

@article{kantelhardt_multifractal_2002,
	title = {Multifractal detrended fluctuation analysis of nonstationary time series},
	volume = {316},
	number = {1-4},
	journal = {Physica A: Statistical Mechanics and its Applications},
	author = {Kantelhardt, Jan W. and Zschiegner, Stephan A. and Koscielny-Bunde, Eva and Havlin, Shlomo and Bunde, Armin and Stanley, H. Eugene},
	year = {2002},
	pages = {87--114},
	file = {Kantelhardt_dfa_2002.pdf:/home/knielbo/Documents/articles/fractals/Kantelhardt_dfa_2002.pdf:application/pdf}
}

@article{hu_effect_2001,
	title = {Effect of trends on detrended fluctuation analysis},
	volume = {64},
	issn = {1063-651X, 1095-3787},
	url = {https://link.aps.org/doi/10.1103/PhysRevE.64.011114},
	doi = {10.1103/PhysRevE.64.011114},
	language = {en},
	number = {1},
	urldate = {2018-02-04},
	journal = {Physical Review E},
	author = {Hu, Kun and Ivanov, Plamen Ch. and Chen, Zhi and Carpena, Pedro and Eugene Stanley, H.},
	month = jun,
	year = {2001},
	file = {hu_trends_2002.pdf:/home/knielbo/Documents/articles/fractals/hu_trends_2002.pdf:application/pdf}
}

@article{chen_effect_2005,
	title = {Effect of nonlinear filters on detrended fluctuation analysis},
	volume = {71},
	url = {https://link.aps.org/doi/10.1103/PhysRevE.71.011104},
	doi = {10.1103/PhysRevE.71.011104},
	number = {1},
	journal = {Phys. Rev. E},
	author = {Chen, Zhi and Hu, Kun and Carpena, Pedro and Bernaola-Galvan, Pedro and Stanley, H. Eugene and Ivanov, Plamen Ch.},
	month = jan,
	year = {2005},
	pages = {011104},
	file = {chen_nonlin_filter_2005.PDF:/home/knielbo/Documents/articles/fractals/chen_nonlin_filter_2005.PDF:application/pdf}
}

@article{peng_mosaic_1994,
	title = {Mosaic organization of {DNA} nucleotides},
	volume = {49},
	number = {2},
	journal = {Physical review e},
	author = {Peng, C.-K. and Buldyrev, Sergey V. and Havlin, Shlomo and Simons, Michael and Stanley, H. Eugene and Goldberger, Ary L.},
	year = {1994},
	pages = {1685},
}

@article{peng_dfa_1994,
    author = {Peng, C.‐K. and Havlin, Shlomo and Stanley, H. Eugene and Goldberger, Ary L.},
    title = "{Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series}",
    journal = {Chaos: An Interdisciplinary Journal of Nonlinear Science},
    volume = {5},
    number = {1},
    pages = {82-87},
    year = {1995},
    month = {03},
    issn = {1054-1500},
    doi = {10.1063/1.166141},
}




@inproceedings{cordeiro_fractal_2015,
	location = {Cham},
	title = {Fractal Beauty in Text},
	isbn = {978-3-319-23485-4},
	doi = {10.1007/978-3-319-23485-4_80},
	series = {Lecture Notes in Computer Science},
	abstract = {This paper assesses if text possesses fractal properties, namely if several attributes that characterize sentences are self-similar. In order to do that, seven corpora were analyzed using several statistical tools, so as to determine if the empirical sequences for the attributes were Gaussian and self-similar. The Kolmogorov-Smirnov goodness-of-fit test and two Hurst parameter estimators were employed. The results show that there is a fractal beauty in the text produced by humans and suggest that its quality is directly proportional to the self-similarity degree.},
	pages = {796--802},
	booktitle = {Progress in Artificial Intelligence},
	publisher = {Springer International Publishing},
	author = {Cordeiro, João and Inácio, Pedro R. M. and Fernandes, Diogo A. B.},
	editor = {Pereira, Francisco and Machado, Penousal and Costa, Ernesto and Cardoso, Amílcar},
	date = {2015},
	year = {2015},
	langid = {english},
	keywords = {Self-similarity in text, Statistical linguistic studies},
}



@inproceedings{gao2016multiscale,
  title={A multiscale theory for the dynamical evolution of sentiment in novels},
  author={Gao, Jianbo and Jockers, Matthew L and Laudun, John and Tangherlini, Timothy},
  booktitle={2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC)},
  pages={1--4},
  year={2016},
  organization={IEEE}
}

@inproceedings{mikolov2018advances,
    title = "Advances in Pre-Training Distributed Word Representations",
    author = "Mikolov, Tomas  and
      Grave, Edouard  and
      Bojanowski, Piotr  and
      Puhrsch, Christian  and
      Joulin, Armand",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://aclanthology.org/L18-1008",
}

@article{cowen2021semantic,
  title={Semantic space theory: A computational approach to emotion},
  author={Cowen, Alan S and Keltner, Dacher},
  journal={Trends in Cognitive Sciences},
	doi = {10.1016/j.tics.2020.11.004},
  volume={25},
  number={2},
  pages={124--136},
  year={2021},
  publisher={Elsevier}
}


@article{mohseni_fractality_2021,
	title = {Fractality and Variability in Canonical and Non-Canonical English Fiction and in Non-Fictional Texts},
	volume = {12},
	issn = {1664-1078},
	url = {https://www.frontiersin.org/article/10.3389/fpsyg.2021.599063},
	abstract = {This study investigates global properties of three categories of English text: canonical fiction, non-canonical fiction, and non-fictional texts. The central hypothesis of the study is that there are systematic differences with respect to structural design features between canonical and non-canonical fiction, and between fictional and non-fictional texts. To investigate these differences, we compiled a corpus containing texts of the three categories of interest, the Jena Corpus of Expository and Fictional Prose ({JEFP} Corpus). Two aspects of global structure are investigated, variability and self-similar (fractal) patterns, which reflect long-range correlations along texts. We use four types of basic observations, (i) the frequency of {POS}-tags per sentence, (ii) sentence length, (iii) lexical diversity, and (iv) the distribution of topic probabilities in segments of texts. These basic observations are grouped into two more general categories, (a) the lower-level properties (i) and (ii), which are observed at the level of the sentence (reflecting linguistic decoding), and (b) the higher-level properties (iii) and (iv), which are observed at the textual level (reflecting comprehension/integration). The observations for each property are transformed into series, which are analyzed in terms of variance and subjected to Multi-Fractal Detrended Fluctuation Analysis ({MFDFA}), giving rise to three statistics: (i) the degree of fractality (H), (ii) the degree of multifractality (D), i.e., the width of the fractal spectrum, and (iii) the degree of asymmetry (A) of the fractal spectrum. The statistics thus obtained are compared individually across text categories and jointly fed into a classification model (Support Vector Machine). Our results show that there are in fact differences between the three text categories of interest. In general, lower-level text properties are better discriminators than higher-level text properties. Canonical fictional texts differ from non-canonical ones primarily in terms of variability in lower-level text properties. Fractality seems to be a universal feature of text, slightly more pronounced in non-fictional than in fictional texts. On the basis of our results obtained on the basis of corpus data we point out some avenues for future research leading toward a more comprehensive analysis of textual aesthetics, e.g., using experimental methodologies.},
	journaltitle = {Frontiers in Psychology},
	author = {Mohseni, Mahdi and Gast, Volker and Redies, Christoph},
	urldate = {2022-02-23},
	year = {2021},
}
@incollection{laaksonen2022hate,
  title={From hate speech recognition to happiness indexing: critical issues in datafication of emotion in text mining},
  author={Laaksonen, Salla-Maaria and P{\"a}{\"a}kk{\"o}nen, Juho and {\"O}hman, Emily},
  booktitle={Handbook of Critical Studies of Artificial Intelligence},
  year={2023},
  publisher={Edward Elgar}
}
@article{teodorescu2023generating,
  title={Generating High-Quality Emotion Arcs For Low-Resource Languages Using Emotion Lexicons},
  author={Teodorescu, Daniela and Mohammad, Saif M},
  journal={arXiv preprint arXiv:2306.02213},
  year={2023}
}
@article{moretti_pamphlet,
author ={Moretti, Franco},
    title = {"Operationalizing"; or, The Function of Measurement in Modern Literary Theory},
    year = {2013},
    urldate = {2022-02-23},
    url = {litlab.stanford.edu/LiteraryLabPamphlet6},
    journaltitle = {Stanford Literary Lab Pamphlet Series},
    }
    
@book{underwood_distant_2019,
	title = {Distant Horizons: Digital Evidence and Literary Change},
	isbn = {978-0-226-61297-3},
	url = {https://www.degruyter.com/document/doi/10.7208/9780226612973/html},
	shorttitle = {Distant Horizons},
	abstract = {Just as a traveler crossing a continent won’t sense the curvature of the earth, one lifetime of reading can’t grasp the largest patterns organizing literary history. This is the guiding premise behind Distant Horizons , which uses the scope of data newly available to us through digital libraries to tackle previously elusive questions about literature.},
	publisher = {University of Chicago Press},
	author = {Underwood, Ted},
	urldate = {2022-02-23},
	date = {2019-02-14},
	year = {2019},
	langid = {english},
	doi = {10.7208/9780226612973},
	note = {Publication Title: Distant Horizons},
	keywords = {American literature, characterization, cultural analytics, diction, digital humanities, distant reading, English literature, gender, genre, machine learning},
}



@inproceedings{alm_characteristics_2010,
	title = {Characteristics of high agreement affect annotation in text},
	eventtitle = {Proceedings of the fourth linguistic annotation workshop},
	pages = {118--122},
	author = {Alm, Cecilia Ovesdotter},
	year = {2010},
}

@inproceedings{alm_emotions_2005,
	title = {Emotions from text: machine learning for text-based emotion prediction},
	pages = {579--586},
	booktitle = {Proceedings of human language technology conference and conference on empirical methods in natural language processing},
	author = {Alm, Cecilia Ovesdotter and Roth, Dan and Sproat, Richard},
	year = {2005},
}





@article{wilkens2012canons,
  title={Canons, close reading, and the evolution of method},
  author={Wilkens, Matthew},
  journal={Debates in the digital humanities},
  pages={249--58},
  year={2012},
  publisher={University of Minnesota Press Minneapolis}
}

@article{murtagh2014ward,
  title={Ward’s hierarchical agglomerative clustering method: which algorithms implement Ward’s criterion?},
  author={Murtagh, Fionn and Legendre, Pierre},
  journal={Journal of classification},
  volume={31},
  number={3},
  pages={274--295},
  year={2014},
  publisher={Springer}
}



@inproceedings{xu2020dombert,
    title = "{D}om{BERT}: Domain-oriented Language Model for Aspect-based Sentiment Analysis",
    author = "Xu, Hu  and
      Liu, Bing  and
      Shu, Lei  and
      Yu, Philip",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.findings-emnlp.156",
    doi = "10.18653/v1/2020.findings-emnlp.156",
    pages = "1725--1731"
}


@article{walsh2021goodreads,
  title={The Goodreads ‘Classics’: A Computational Study of Readers, Amazon, and Crowdsourced Amateur Criticism},
  author={Walsh, Melanie and Antoniak, Maria},
  journal={Journal of Cultural Analytics},
  volume={4},
  pages={243--287},
  year={2021},
  publisher={Department of Languages, Literatures, and Cultures}
}

@book{moretti2013distant,
  title={Distant reading},
  author={Moretti, Franco},
  year={2013},
  publisher={Verso Books}
}

@inproceedings{van2015identifying,
  title={Identifying literary texts with bigrams},
  author={van Cranenburgh, Andreas and Koolen, Corina},
  booktitle={Proceedings of the Fourth Workshop on Computational Linguistics for Literature},
  pages={58--67},
  year={2015}
}

@article{bose2015generalized,
  title={Generalized quadratic discriminant analysis},
  author={Bose, Smarajit and Pal, Amita and SahaRay, Rita and Nayak, Jitadeepa},
  journal={Pattern Recognition},
  volume={48},
  number={8},
  pages={2676--2684},
  year={2015},
  publisher={Elsevier}
}

@article{rezaeinia2019sentiment,
  title={Sentiment analysis based on improved pre-trained word embeddings},
  author={Rezaeinia, Seyed Mahdi and Rahmani, Rouhollah and Ghodsi, Ali and Veisi, Hadi},
  journal={Expert Systems with Applications},
  volume={117},
  pages={139--147},
  year={2019},
  publisher={Elsevier},
    doi = {10.1016/j.eswa.2018.08.044}
}

@article{giatsoglou2017sentiment,
  title={Sentiment analysis leveraging emotions and word embeddings},
  author={Giatsoglou, Maria and Vozalis, Manolis G and Diamantaras, Konstantinos and Vakali, Athena and Sarigiannidis, George and Chatzisavvas, Konstantinos Ch},
  journal={Expert Systems with Applications},
  volume={69},
  pages={214--224},
  year={2017},
  publisher={Elsevier},
    doi = {10.1016/j.eswa.2016.10.043}
}

@inproceedings{ho1995random, 
  title={Random decision forests}, 
  author={Ho, Tin Kam}, 
  booktitle={Proceedings of 3rd international conference on document analysis and recognition}, 
  volume={1}, 
  pages={278--282}, 
  year={1995}, 
  organization={IEEE} 
}

@inproceedings{chan1982updating,
  title={Updating formulae and a pairwise algorithm for computing sample variances},
  author={Chan, Tony F and Golub, Gene H and LeVeque, Randall J},
  booktitle={COMPSTAT 1982 5th Symposium held at Toulouse 1982},
  pages={30--41},
  year={1982},
  organization={Springer}
}

@article{swain1977decision,
  title={The decision tree classifier: Design and potential},
  author={Swain, Philip H and Hauska, Hans},
  journal={IEEE Transactions on Geoscience Electronics},
  volume={15},
  number={3},
  pages={142--147},
  year={1977},
  publisher={IEEE}
}

@article{bao2016boosted,
  title={Boosted near-miss under-sampling on SVM ensembles for concept detection in large-scale imbalanced datasets},
  author={Bao, Lei and Juan, Cao and Li, Jintao and Zhang, Yongdong},
  journal={Neurocomputing},
  volume={172},
  pages={198--206},
  year={2016},
  publisher={Elsevier}
}

@article{chawla2002smote,
  title={SMOTE: synthetic minority over-sampling technique},
  author={Chawla, Nitesh V and Bowyer, Kevin W and Hall, Lawrence O and Kegelmeyer, W Philip},
  journal={Journal of artificial intelligence research},
  volume={16},
  pages={321--357},
  year={2002}
}

@inproceedings{mani2003knn,
  title={kNN approach to unbalanced data distributions: a case study involving information extraction},
  author={Mani, Inderjeet and Zhang, I},
  booktitle={Proceedings of workshop on learning from imbalanced datasets},
  volume={126},
  pages={1--7},
  year={2003},
  organization={ICML}
}



@inproceedings{bird2006nltk,
  title={NLTK: the natural language toolkit},
  author={Bird, Steven},
  booktitle={Proceedings of the COLING/ACL 2006 Interactive Presentation Sessions},
  pages={69--72},
  year={2006}
}


@inproceedings{textlabs,
  title={US Novel Corpus},
  author={Textual Optic Labs},
  booktitle={University of Chicago}
}




@inproceedings{liu2019early,
  title={Early prediction of sepsis via smote upsampling and mutual information based downsampling},
  author={Liu, Shiyu and Ong, Ming Lun and Mun, Kar Kin and Yao, Jia and Motani, Mehul},
  booktitle={2019 Computing in Cardiology (CinC)},
  pages={Page--1},
  year={2019},
  organization={IEEE}
}

@inproceedings{rustogi2019swift,
  title={Swift imbalance data classification using SMOTE and extreme learning machine},
  author={Rustogi, Rishabh and Prasad, Ayush},
  booktitle={2019 International Conference on Computational Intelligence in Data Science (ICCIDS)},
  pages={1--6},
  year={2019},
  organization={IEEE}
}

@article{koolen2020literary,
  title={Literary quality in the eye of the Dutch reader: The National Reader Survey},
  author={Koolen, Corina and van Dalen-Oskam, Karina and van Cranenburgh, Andreas and Nagelhout, Erica},
  journal={Poetics},
  volume={79},
  pages={101439},
  year={2020},
  publisher={Elsevier}
}

@article{riddell_readers_2018,
	title = {Readers and their roles: {Evidence} from readers of contemporary fiction in the {Netherlands}},
	volume = {13},
	issn = {1932-6203},
	shorttitle = {Readers and their roles},
	url = {https://dx.plos.org/10.1371/journal.pone.0201157},
	doi = {10.1371/journal.pone.0201157},
	language = {en},
	number = {7},
	urldate = {2022-09-15},
	journal = {PLOS ONE},
	author = {Riddell, Allen and van Dalen-Oskam, Karina},
	editor = {Wray, K. Brad},
	month = jul,
	year = {2018},
	pages = {e0201157}
 }


@article{febres_quantifying_2017,
	title = {Quantifying literature quality using complexity criteria},
	volume = {24},
	issn = {0929-6174, 1744-5035},
	url = {http://arxiv.org/abs/1401.7077},
	doi = {10.1080/09296174.2016.1169847},
	abstract = {We measured entropy and symbolic diversity for English and Spanish texts including literature Nobel laureates and other famous authors. Entropy, symbol diversity and symbol frequency profiles were compared for these four groups. We also built a scale sensitive to the quality of writing and evaluated its relationship with the Flesch's readability index for English and the Szigriszt's perspicuity index for Spanish. Results suggest a correlation between entropy and word diversity with quality of writing. Text genre also influences the resulting entropy and diversity of the text. Results suggest the plausibility of automated quality assessment of texts.},
	number = {1},
	urldate = {2022-09-22},
	journal = {Journal of Quantitative Linguistics},
	author = {Febres, Gerardo and Jaffe, Klaus},
	month = jan,
	year = {2017},
	note = {arXiv:1401.7077 [cs]},
	keywords = {Computer Science - Computation and Language},
	pages = {16--53}
}


@article{kuijpers_understanding_2018,
	title = {Understanding and {Appreciating} {Literary} {Texts} {Through} {Rereading}},
	volume = {55},
	issn = {0163-853X, 1532-6950},
	url = {https://www.tandfonline.com/doi/full/10.1080/0163853X.2017.1390352},
	doi = {10.1080/0163853X.2017.1390352},
	language = {en},
	number = {7},
	urldate = {2023-04-17},
	journal = {Discourse Processes},
	author = {Kuijpers, Moniek M. and Hakemulder, Frank},
	month = oct,
	year = {2018},
	pages = {619--641}
}


@incollection{van_peer_ideology_2008,
	address = {Amsterdam ; Philadelphia},
	title = {Ideology or aesthetic quality?},
	language = {en},
	booktitle = {The quality of literature: linguistic studies in literary evaluation},
	publisher = {John Benjamins Publishing},
	author = {van Peer, Willie},
	editor = {van Peer, Willie},
	year = {2008},
	pages = {17--29}
}


@inproceedings{veleski_weak_2020,
	address = {Amsterdam, NETHERLANDS, THE},
	title = {Weak {Negative} {Correlation} between the {Present} {Day} {Popularity} and the {Mean} {Emotional} {Valence} of {Late} {Victorian} {Novels}},
	abstract = {Despite the recent upswing of computational research on Victorian novels, it has largely overlooked insight from cultural evolution and the cognitive sciences. This study aims to contribute to this incipient scholarship by testing the hypothesis that novels containing content with a lower mean emotional valence are more likely to trigger recommendation-based transmission chains, and as a result tend to have greater cultural longevity. This study performs a correlation analysis between the mean sentiment and the contemporary popularity (using the number of user ratings from Goodreads) of a selection of late Victorian novels published in the United Kingdom between 1891 and 1901, taken from Project Gutenberg (n=846). Moreover, the study looks into the implications of this correlation for the differences between novels that were bestsellers at the time of publication and those that can be considered canonical today (that have recently had Broadview, Oxford University, or Penguin Press editions). The results show a weak negative correlation between the present day popularity and the mean emotional valence of the novels, which nevertheless holds true for both the bestselling and canonical novels. Moreover, canonical novels tend to have a lower mean emotional valence than the bestsellers.},
	language = {en},
	booktitle = {{CEUR} {Workshop} {Proceedings}},
	publisher = {CEUR},
	author = {Veleski, Stefan},
	year = {2020},
	pages = {32--43}
}

@article{wellek_denial_of_quality_1972,
	title = {The {Attack} on {Literature}},
	volume = {42},
	issn = {0003-0937},
	url = {https://www.jstor.org/stable/41207073},
	number = {1},
	urldate = {2023-04-17},
	journal = {The American Scholar},
	author = {Wellek, René},
	year = {1972},
	note = {Publisher: The Phi Beta Kappa Society},
	pages = {27--42}
}


@article{harrison_re-reading_2018,
	title = {Re-reading in stylistics},
	volume = {27},
	issn = {0963-9470},
	url = {https://doi.org/10.1177/0963947018792719},
	doi = {10.1177/0963947018792719},
	abstract = {Cognitive stylistics is primarily concerned with the cognitive processes ? mental simulations ? experienced by readers. Most cognitive stylisticians agree that experiences of reading texts are dynamic and flexible. Changes in the context of reading, our attentional focus on a given day, our extra background knowledge about the text, and so on, are all factors that contribute to our experience of a fictional world. A second reading of a text is a different experience to a first reading.As researchers begin to systematically distinguish between the ?solitary? and ?social? readings that constitute reading as a phenomenon (Peplow et al., 2016), the relationship between multiple readings and the nature of their processing becomes increasingly pertinent. In order to explore this relationship, firstly we examine the different ways in which re-reading has previously been discussed in stylistics, grounding our claims in an empirical analysis of articles published in key stylistics journals over the past two decades. Next, we draw on reader response data from an online questionnaire in order to assess the role of re-reading and the motivations that underpin it. Finally, we describe an exercise for the teaching of cognitive stylistics, specifically applying schema theory in literary linguistic analysis (Cook, 1994), which illustrates the need to distinguish between readings as part of an analysis. Through these three sections we argue that our experiences of texts should be considered diachronically, and propose that the different readings that make up an analysis of a text should be given greater attention in stylistic research and teaching.},
	number = {3},
	urldate = {2023-04-15},
	journal = {Language and Literature},
	author = {Harrison, Chloe and Nuttall, Louise},
	month = aug,
	year = {2018},
	note = {Publisher: SAGE Publications Ltd},
	pages = {176--195}
}


@misc{karlyn_chadwyck-healey_nodate,
	title = {Chadwyck-{Healey} {Literature} {Collection}},
	url = {http://collections.chadwyck.com/marketing/products/about_ilc.jsp?collection=ncf},
	urldate = {2023-05-06},
	author = {Karlyn, Danny and Keymer, Tom},
	file = {Chadwyck-Healey Literature Collections - Marketing Site:/Users/au324704/Zotero/storage/BCDEBNZH/about_ilc.html:text/html},
}


@book{bloom_western_1995,
	address = {New York, NY},
	edition = {First Riverhead Edition},
	title = {The {Western} {Canon}: {The} {Books} and {School} of the {Ages}},
	isbn = {978-1-57322-514-4},
	shorttitle = {The {Western} {Canon}},
	abstract = {NATIONAL BESTSELLERNOMINATED FOR THE NATIONAL BOOK CRITICS CIRCLE AWARD“Heroically brave, formidably learned… The Western Canon is a passionate demonstration of why some writers have triumphantly escaped the oblivion in which time buries almost all human effort. It inspires hope… that what humanity has long cherished, posterity will also.” –The New York Times Book ReviewLiterary critic Harold Bloom's The Western Canon is more than a required reading list -- it is a vision. Infused with a love of learning, compelling in its arguments for a unifying written culture, it argues brilliantly against the politicization of literature and presents a guide to the great works of the western literary tradition and essential writers of the ages: the "Western Canon." Harold Bloom's book, much-discussed and praised in publications as diverse as The Economist and Entertainment Weekly, offers a dazzling display of erudition mixed with passion. For years to come it will serve as an inspiration to return to the joys of reading our literary tradition offers us.},
	language = {English},
	publisher = {Riverhead Books},
	author = {Bloom, Harold},
	month = sep,
	year = {1995},
}

@book{bourdieu_rules_1996,
	address = {Stanford},
	title = {The {Rules} of {Art}: {Genesis} and {Structure} of the {Literary} {Field}},
	isbn = {978-0-8047-2568-2},
	shorttitle = {The {Rules} of {Art}},
	abstract = {Written with verve and intensity (and a good bit of wordplay), this is the long-awaited study of Flaubert and the modern literary field that constitutes the definitive work on the sociology of art by one of the world’s leading social theorists. Drawing upon the history of literature and art from the mid-nineteenth century to the present, Bourdieu develops an original theory of art conceived as an autonomous value. He argues powerfully against those who refuse to acknowledge the interconnection between art and the structures of social relations within which it is produced and received. As Bourdieu shows, art’s new autonomy is one such structure, which complicates but does not eliminate the interconnection.The literary universe as we know it today took shape in the nineteenth century as a space set apart from the approved academies of the state. No one could any longer dictate what ought to be written or decree the canons of good taste. Recognition and consecration were produced in and through the struggle in which writers, critics, and publishers confronted one another.},
	publisher = {Stanford University Press},
	author = {Bourdieu, Pierre},
	translator = {Emanuel, Susan},
	year = {1996},
	keywords = {Literary Studies -- General Criticism and Theory, Philosophy -- Social Theory, Sociology -- Theory and Methods},
}


@book{guillory_cultural_1995,
	address = {Chicago, IL},
	title = {Cultural {Capital}: {The} {Problem} of {Literary} {Canon} {Formation}},
	isbn = {978-0-226-31044-2},
	shorttitle = {Cultural {Capital}},
	url = {https://press.uchicago.edu/ucp/books/book/chicago/C/bo3634644.html},
	abstract = {John Guillory challenges the most fundamental premises of the canon debate by resituating the problem of canon formation in an entirely new theoretical framework. The result is a book that promises to recast not only the debate about the literary curriculum but also the controversy over "multiculturalism" and the current "crisis of the humanities." Employing concepts drawn from Pierre Bourdieu’s sociology, Guillory argues that canon formation must be understood less as a question of the representation of social groups than as a question of the distribution of "cultural capital" in the schools, which regulate access to literacy, to the practices of reading and writing.},
	language = {en},
	urldate = {2023-05-06},
	publisher = {University of Chicago Press},
	author = {Guillory, John},
	month = mar,
	year = {1995},
	keywords = {academic, books, capitalism, case study, class, classics, college, controversial, crisis, criticism, culture, debate, discourse, english major, expert, famous authors, gray, groups, historical, history, humanities, interdisciplinary, literature, milton, multicultural, multiculturalism, novels, race, reader, reading, representation, scholarly, sexuality, sociology, textbook, theoretical, university, vernacular, wordsworth},
}



@book{archer_bestseller_2017,
	address = {London},
	title = {The bestseller code},
	isbn = {978-0-14-198248-9},
	abstract = {Ask most people about massive success in the world of fiction, and you’ll typically hear that it’s a game of hazy crystal balls. The sales figures of E. L. James or Dan Brown seem to be freakish―random occurrences in an unknowable market. But what if there were an algorithm that could reveal a secret DNA of bestsellers, regardless of their genre? What if it knew, just from analyzing the words alone, not just why genre writers like John Grisham and Danielle Steel belong on the lists, but also that authors such as Junot Diaz, Jodi Picoult, and Donna Tartt had telltale signs of success all over their pages? Thanks to Jodie Archer and Matthew Jockers, the algorithm exists, the code has been cracked, and the results bring fresh new insights into how fiction works and why we read. The Bestseller Code offers a new theory for why Fifty Shades of Grey sold so well. It sheds light on the current craze for dark heroines. It reveals which themes tend to sell best. And all with fascinating supporting data taken from a five-year study of twenty thousand novels. Then there is the hunt for "the one"―the paradigmatic example of bestselling writing according to a computer's analysis of thousands of points of data. The result is surprising, a bit ironic, and delightfully unorthodox. This book explains groundbreaking text-mining research in accessible terms and offers a new perspective on the New York Times bestseller list. It's a big-idea book about the relationship between creativity and technology that will be provocative to anyone interested in how analytics have already transformed the worlds of finance, medicine, and sports. But at heart it is a celebration of books for readers and writers―a compelling investigation into how successful writing works, and a fresh take on our intellectual and emotional response to stories},
	language = {eng},
	publisher = {Penguin books},
	author = {Archer, Jodie and Jockers, Matthew Lee},
	year = {2017},
}

@book{porter_stanford_2018,
	title = {Stanford {Literary} {Lab} {Pamphlet} 17: {Popularity}/{Prestige}},
	url = {https://litlab.stanford.edu/LiteraryLabPamphlet17.pdf},
	urldate = {2023-04-19},
	publisher = {Stanford Literary Lab},
	author = {Porter, J.D.},
	year = {2018}
}

@misc{vulture_canon_premature_2018,
	title = {A {Premature} {Attempt} at the 21st {Century} {Literary} {Canon}},
	url = {https://www.vulture.com/article/best-books-21st-century-so-far.html},
	abstract = {Wherein a panel of learned critics tells us what belongs on a list of the 100 most important books of the 2000s … so far.},
	language = {en-us},
	urldate = {2023-04-20},
	journal = {Vulture},
	author = {Vulture, editors},
	month = sep,
	year = {2018}
}




@book{algee-hewitt_canonarchive_2016,
	title = {Canon/{Archive}. {Large}-scale {Dynamics} in the {Literary} {Field}},
	url = {https://litlab.stanford.edu/LiteraryLabPamphlet11.pdf},
	urldate = {2023-05-04},
	publisher = {Stanford Literary Lab},
	author = {Algee-Hewitt, Mark and Allison, Sarah and Gemma, Marissa and Heuser, Ryan and Moretti, Franco and Walser, Hannah},
	year = {2016},
	file = {LiteraryLabPamphlet11.pdf:/Users/au324704/Zotero/storage/KYZGGIZ7/LiteraryLabPamphlet11.pdf:application/pdf},
}


@book{bourdieu_field_1993,
	address = {New York},
	title = {The field of cultural production: essays on art and literature},
	isbn = {978-0-231-08286-0 978-0-231-08287-7},
	shorttitle = {The field of cultural production},
	publisher = {Columbia University Press},
	author = {Bourdieu, Pierre},
	editor = {Johnson, Randal},
	year = {1993},
	keywords = {Aesthetics, French, Arts, Arts and society, Arts audiences, Economic aspects, France},
	file = {1993_bourdieu_fieldofculturalproduction.pdf:/Users/au324704/Zotero/storage/2HZBWBGU/1993_bourdieu_fieldofculturalproduction.pdf:application/pdf},
}


@article{fowler_genre_1979,
	title = {Genre and the {Literary} {Canon}},
	volume = {11},
	issn = {0028-6087},
	url = {https://www.jstor.org/stable/468873},
	doi = {10.2307/468873},
	number = {1},
	urldate = {2023-04-20},
	journal = {New Literary History},
	author = {Fowler, Alastair},
	year = {1979},
	note = {Publisher: Johns Hopkins University Press},
	pages = {97--119},
	file = {JSTOR Full Text PDF:/Users/au324704/Zotero/storage/RDWD2NCY/Fowler - 1979 - Genre and the Literary Canon.pdf:application/pdf},
}


@misc{manshel_who_2019,
	title = {Who {Cares} about {Literary} {Prizes}?},
	url = {https://www.publicbooks.org/who-cares-about-literary-prizes/},
	abstract = {Earlier this morning, the Booker Prize judges announced their shortlist for 2019, stamping just six novels—out of the ...},
	language = {en-US},
	urldate = {2023-05-05},
	journal = {Public Books},
	author = {Manshel, Alexander and McGrath, Laura B and Porter, J.D.},
	month = sep,
	year = {2019},
	file = {Snapshot:/Users/au324704/Zotero/storage/YPA9EZPV/who-cares-about-literary-prizes.html:text/html},
}


@article{sapiro_literary_2003,
	title = {The literary field between the state and the market},
	volume = {31},
	issn = {0304422X},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0304422X03000482},
	doi = {10.1016/j.poetic.2003.09.001},
	abstract = {According to Pierre Bourdieu’s analysis, the emergence of the literary ﬁeld results from a historical process by which the literary activity became autonomous from diﬀerent types of external constraints, related to the conditions of production. This paper gives a provisional account of these types of constraints, focusing on the French case. It is possible to class the national literary ﬁelds according to their dependence upon the State or upon the market. In authoritarian regimes, the State is an instrument of control put at the service of an ideological system. The ideological demand determines the supply of cultural goods. It implies a strict regulation of economic exchanges, a centralisation of the means of production and consecration. The liberalization of the bookmarket has favoured the literary ﬁeld’s relative liberation from State control. But the market has also constraints of its own, mainly proﬁt, which encloses, for literature, the risk of standardization. During the 19th Century, writers claimed for the autonomy of literary recognition, that must be grounded on aesthetical judgments and not on sales. In France, the State progressively became committed to help literary activities preserve a certain degree of autonomy from the market.},
	language = {en},
	number = {5-6},
	urldate = {2023-05-05},
	journal = {Poetics},
	author = {Sapiro, Gisèle},
	month = oct,
	year = {2003},
	pages = {441--464}
}


@book{casanova_world_2007,
	address = {Cambridge, MA},
	series = {Convergences: {Inventories} of the {Present}},
	title = {The {World} {Republic} of {Letters}},
	isbn = {978-0-674-01021-5},
	shorttitle = {The {World} {Republic} of {Letters}},
	abstract = {The “world of letters” has always seemed a matter more of metaphor than of global reality. In this book, Pascale Casanova shows us the state of world literature behind the stylistic refinements—a world of letters relatively independent from economic and political realms, and in which language systems, aesthetic orders, and genres struggle for dominance. Rejecting facile talk of globalization, with its suggestion of a happy literary “melting pot,” Casanova exposes an emerging regime of inequality in the world of letters, where minor languages and literatures are subject to the invisible but implacable violence of their dominant counterparts.
Inspired by the writings of Fernand Braudel and Pierre Bourdieu, this ambitious book develops the first systematic model for understanding the production, circulation, and valuing of literature worldwide. Casanova proposes a baseline from which we might measure the newness and modernity of the world of letters—the literary equivalent of the meridian at Greenwich. She argues for the importance of literary capital and its role in giving value and legitimacy to nations in their incessant struggle for international power. Within her overarching theory, Casanova locates three main periods in the genesis of world literature—Latin, French, and German—and closely examines three towering figures in the world republic of letters—Kafka, Joyce, and Faulkner. Her work provides a rich and surprising view of the political struggles of our modern world—one framed by sites of publication, circulation, translation, and efforts at literary annexation.},
	publisher = {Harvard University Press},
	author = {Casanova, Pascale},
	month = apr,
	year = {2007}
}

@book{moretti_graphs_2007,
	address = {London New York},
	edition = {Illustrated edition},
	title = {Graphs, {Maps}, {Trees}: {Abstract} {Models} for {Literary} {History}},
	isbn = {978-1-84467-185-4},
	shorttitle = {Graphs, {Maps}, {Trees}},
	abstract = {In this groundbreaking book, Franco Moretti argues that literature scholars should stop reading books and start counting, graphing, and mapping them instead. In place of the traditionally selective literary canon of a few hundred texts, Moretti offers charts, maps and time lines, developing the idea of “distant reading” into a full-blown experiment in literary historiography, in which the canon disappears into the larger literary system. Charting entire genres—the epistolary, the gothic, and the historical novel—as well as the literary output of countries such as Japan, Italy, Spain, and Nigeria, he shows how literary history looks significantly different from what is commonly supposed and how the concept of aesthetic form can be radically redefined.},
	language = {English},
	publisher = {Verso},
	author = {Moretti, Franco},
	month = sep,
	year = {2007},
}

@book{bennett_popular_1990,
	title = {Popular {Fiction}: {Technology}, {Ideology}, {Production}, {Reading}},
	isbn = {978-0-415-02517-1},
	shorttitle = {Popular {Fiction}},
	abstract = {Literatuuropgave : p. 461-474 Met reg. A collection of essays on popular fiction, including cinema, broadcasting institutions and the publishing industry. The essays are divided in different sections, each of them focuses on a different aspect of either its constitution or the forms of its social attributions. Subjects are, among others, popular fictions and cultural technologies, fictioning the nation, gender and fiction, the production of fiction.},
	language = {en},
	publisher = {Routledge},
	author = {Bennett, Tony},
	year = {1990},
	note = {Google-Books-ID: cAcqAQAAIAAJ},
}


@incollection{tygstrup_quality_2018,
	title = {Culture, quality, and human time},
	url = {https://interaccio.diba.cat/sites/interaccio.diba.cat/files/contested_qualities_0.pdf#page=48},
	urldate = {2023-03-31},
	booktitle = {Contested {Qualities}},
	publisher = {Fagbokforlaget},
	author = {Tygstrup, Frederik},
	editor = {Eliassen, Knut Ove and Hovden, Jan and Prytz, Øyvind},
	year = {2018},
	pages = {93--104},
}



@book{koolen_reading_2018,
	address = {Amsterdam},
	series = {{ILLC} dissertation series},
	title = {Reading beyond the female: the relationship between perception of author gender and literary quality},
	isbn = {978-94-028-0951-0},
	shorttitle = {Reading beyond the female},
	abstract = {This thesis combines methods from sociology of literature and natural language processing to answer the questions: what is the relationship between author gender and the perceived literary quality of her work? And to what extent can textual qualities be ascribed to author gender? I first assess Dutch female authors' chances of gaining literary prestige. Even though female authors publish many literary works, they still have a harder time climbing the literary ladder. Results of the 2013 National Reader Survey mirror this skewness. Respondents were asked to supply ratings of literary quality, on a list of 401 recent, bestselling Dutch-language novels in several genres (the Riddle of Literary Quality corpus). Even within genre works by female authors' are judged to be of lesser quality, and 'feminine' novels are seen as the worst; formulaic detectives score better. Female author gender is not a conscious motivation, but analysis of respondents' motivations shows that instead, the text is associated with 'femaleness' - through genre, topics or style. Such associations lead to perception of lower literary quality. I then analyze the text of the novels themselves to examine to which extent such femaleness of text exists. First, computational analysis of the Riddle corpus indicates that author gender is too easily assumed to be the cause of text differences. Moreover, additional visualizations show that gender group differences are often caused by outliers. In the final chapters, I focus on a 'feminine' topic, attention to characters' physical appearance. I show that it is not exclusive to the genre of chick-lit, nor to female authors. In fact, male literary authors write most about physical appearance, in describing female love interests. This novel approach shows that female author gender is connected to the text differently than expected. By reading differently, literary quality can be judged separate from female author gender},
	language = {en},
	number = {DS-2018-03},
	publisher = {Institute for Logic, Language and Computation, Universiteit van Amsterdam},
	author = {Koolen, Cornelia Wilhelmina},
	year = {2018}
}


@inproceedings{lassen_reviewer_2022,
	address = {Antwerp, Belgium},
	title = {Reviewer {Preferences} and {Gender} {Disparities} in {Aesthetic} {Judgments}},
	url = {https://ceur-ws.org/Vol-3290/short_paper1885.pdf},
	abstract = {Aesthetic preferences are considered highly subjective resulting in inherently noisy judgements of aesthetic objects, yet certain aspects of aesthetic judgement display convergent trends over time. This paper present a study that uses literary reviews as a proxy for aesthetic judgement in order to identify systematic components that can be attributed to bias. Speciﬁcally we ﬁnd that judgement of literary quality in newspapers displays a gender bias in preference of male writers. Male reviewers have a same gender preference while female reviewer show an opposite gender preference. While alternative accounts exist of this apparent gender disparity, we argue that it reﬂects a cultural gender antagonism.},
	language = {en},
	urldate = {2022-09-06},
	booktitle = {{CEUR} {Workshop} {Proceedings}},
	author = {Lassen, Ida Marie Schytt and Bizzoni, Yuri and Peura, Telma and Thomsen, Mads Rosendahl and Nielbo, Kristoffer Laigaard},
	year = {2022},
	keywords = {Computer Science - Computers and Society},
	pages = {280--290}
}


@inproceedings{jannatus_saba_study_2021,
	address = {Online},
	title = {A {Study} on {Using} {Semantic} {Word} {Associations} to {Predict} the {Success} of a {Novel}},
	url = {https://aclanthology.org/2021.starsem-1.4},
	doi = {10.18653/v1/2021.starsem-1.4},
	abstract = {Many new books get published every year, and only a fraction of them become popular among the readers. So the prediction of a book success can be a very useful parameter for publishers to make a reliable decision. This article presents the study of semantic word associations using the word embedding of book content for a set of Roget's thesaurus concepts for book success prediction. In this work, we discuss the method to represent a book as a spectrum of concepts based on the association score between its content embedding and a global embedding (i.e. fastText) for a set of semantically linked word clusters. We show that the semantic word associations outperform the previous methods for book success prediction. In addition, we present that semantic word associations also provide better results than using features like the frequency of word groups in Roget's thesaurus, LIWC (a popular tool for linguistic inquiry and word count), NRC (word association emotion lexicon), and part of speech (PoS). Our study reports that concept associations based on Roget's Thesaurus using word embedding of individual novel resulted in the state-of-the-art performance of 0.89 average weighted F1-score for book success prediction. Finally, we present a set of dominant themes that contribute towards the popularity of a book for a specific genre.},
	urldate = {2023-04-10},
	booktitle = {Proceedings of *{SEM} 2021: {The} {Tenth} {Joint} {Conference} on {Lexical} and {Computational} {Semantics}},
	publisher = {Association for Computational Linguistics},
	author = {Jannatus Saba, Syeda and Bijoy, Biddut Sarker and Gorelick, Henry and Ismail, Sabir and Islam, Md Saiful and Amin, Mohammad Ruhul},
	month = aug,
	year = {2021},
	pages = {38--51}
}

@incollection{hamm_literary_quality_historical_2018,
	title = {Literary Quality: Historical Perspectives},
	url = {https://interaccio.diba.cat/sites/interaccio.diba.cat/files/contested_qualities_0.pdf#page=48},
	urldate = {2023-03-31},
	booktitle = {Contested {Qualities}},
	publisher = {Fagbokforlaget},
	author = {Bjerck Hagen, Eric and Hamm, Christine and Helmich Pedersen, Frode and Sejersted, Jørgen Magnus and Vassenden, Eirik},
	editor = {Eliassen, Knut Ove and Hovden, Jan and Prytz, Øyvind},
	year = {2018},
	pages = {47--74},
}


@article{porter_popularityprestige_2018,
	title = {Popularity/Prestige: A New Canon},
	url = {https://litlab.stanford.edu/pop-pres-ny/},
	abstract = {The Stanford Literary Lab is a research collective that applies computational criticism, in all its forms, to the study of literature.},
	urldate = {2023-05-22},
	journal = {Stanford Literary Lab},
	author = {Porter, J.D.}, 
	year = {2018},
	file = {Snapshot:/Users/au324704/Zotero/storage/4IT2WQAS/pop-pres-ny.html:text/html},
}


@article{van2020results,
  title={Results of a single blind literary taste test with short anonymized novel fragments},
  author={van Cranenburgh, Andreas and Koolen, Corina},
  journal={arXiv preprint arXiv:2011.01624},
  year={2020}
}

@inproceedings{campbell2006elements,
  title={The elements of style},
  author={Campbell-Kibler, Kathryn and Eckert, Penelope and Mendoza-Denton, Norma and Moore, Emma},
  booktitle={Poster presented at New Ways of Analyzing Variation},
  volume={35},
  year={2006}
}


@article{duval_nobel_2005,
	title = {A {Nobel} calling: 100 years of controversy},
	shorttitle = {A {Nobel} calling},
	url = {https://www.independent.co.uk/news/world/europe/a-nobel-calling-100-years-of-controversy-319509.html},
	abstract = {Not many women, a weakness for Anglo-Saxon literature and an ostrich-like ability to resist popular or political pressure. Alex Duval Smith reports from Stockholm on the strange and secret world of the Swedish Academy},
	language = {en},
	urldate = {2023-05-26},
	journal = {The Independent},
	author = {Duval, Alex},
	month = oct,
	year = {2005},
	note = {Section: News},
	file = {Snapshot:/Users/au324704/Zotero/storage/ID9ZJ2MG/a-nobel-calling-100-years-of-controversy-319509.html:text/html},
}


@article{epstein_nobel_2012,
	chapter = {Opinion},
	title = {The {Nobel} {Prize} for {Political} {Literature}},
	issn = {0099-9660},
	shorttitle = {Joseph {Epstein}},
	url = {http://online.wsj.com/article/SB10000872396390444799904578054821709524326.html},
	abstract = {in The Wall Street Journal, Joseph Epstein writes that Tolstoy and Twain never won, but many obscure writers have—criteria other than high art seem to be involved.},
	language = {en-US},
	urldate = {2023-05-26},
	journal = {Wall Street Journal},
	author = {Epstein, Joseph},
	month = oct,
	year = {2012},
	file = {Snapshot:/Users/au324704/Zotero/storage/FV85TVLU/SB10000872396390444799904578054821709524326.html:text/html},
}


@inproceedings{mohammad_once_2011,
	address = {Portland, OR, USA},
	title = {From {Once} {Upon} a {Time} to {Happily} {Ever} {After}: {Tracking} {Emotions} in {Novels} and {Fairy} {Tales}},
	shorttitle = {From {Once} {Upon} a {Time} to {Happily} {Ever} {After}},
	url = {https://aclanthology.org/W11-1514},
	urldate = {2022-10-09},
	booktitle = {Proceedings of the 5th {ACL}-{HLT} {Workshop} on {Language} {Technology} for {Cultural} {Heritage}, {Social} {Sciences}, and {Humanities}},
	publisher = {Association for Computational Linguistics},
	author = {Mohammad, Saif},
	month = jun,
	year = {2011},
	pages = {105--114},
	file = {Full Text PDF:/Users/au324704/Zotero/storage/QNNYIMLY/Mohammad - 2011 - From Once Upon a Time to Happily Ever After Track.pdf:application/pdf},
}


@article{von_hallberg_editors_1983,
	title = {Editor's {Introduction}},
	volume = {10},
	issn = {0093-1896},
	url = {https://www.jstor.org/stable/1343403},
	number = {1},
	urldate = {2023-01-05},
	journal = {Critical Inquiry},
	author = {von Hallberg, Robert},
	year = {1983},
	note = {Publisher: The University of Chicago Press},
	pages = {iii--vi},
	file = {JSTOR Full Text PDF:/Users/au324704/Zotero/storage/EM95XLP9/von Hallberg - 1983 - Editor's Introduction.pdf:application/pdf},
}


@inproceedings{ohman_validity_2021,
	address = {NIT Silchar, India},
	title = {The {Validity} of {Lexicon}-based {Sentiment} {Analysis} in {Interdisciplinary} {Research}},
	url = {https://aclanthology.org/2021.nlp4dh-1.2},
	abstract = {Lexicon-based sentiment and emotion analysis methods are widely used particularly in applied Natural Language Processing (NLP) projects in fields such as computational social science and digital humanities. These lexicon-based methods have often been criticized for their lack of validation and accuracy – sometimes fairly. However, in this paper, we argue that lexicon-based methods work well particularly when moving up in granularity and show how useful lexicon-based methods can be for projects where neither qualitative analysis nor a machine learning-based approach is possible. Indeed, we argue that the measure of a lexicon's accuracy should be grounded in its usefulness.},
	urldate = {2023-03-18},
	booktitle = {Proceedings of the {Workshop} on {Natural} {Language} {Processing} for {Digital} {Humanities}},
	publisher = {NLP Association of India (NLPAI)},
	author = {{\"O}hman, Emily},
	month = dec,
	year = {2021},
	pages = {7--12},
	file = {Full Text PDF:/Users/au324704/Zotero/storage/LZIR7QQ5/{\"O}hman - 2021 - The Validity of Lexicon-based Sentiment Analysis i.pdf:application/pdf},
}


@misc{jannesar_goodreads_2018,
	title = {Goodreads {Book} {Datasets} {With} {User} {Rating} {2M}},
	url = {https://www.kaggle.com/datasets/b2dde9353c9d10c36e4d6b593a74c109dbaca6393a1ca0f2c7abafeba7633641},
	abstract = {Every 2 days , this dataset will be updated},
	language = {en},
	urldate = {2022-12-16},
	author = {Jannesar, Bahram},
	year = {2018},
	file = {Snapshot:/Users/au324704/Zotero/storage/I8MJG49S/goodreads-book-datasets-10m.html:text/html},
}


@article{kantelhardt_detecting_2001,
	title = {Detecting long-range correlations with detrended fluctuation analysis},
	volume = {295},
	issn = {0378-4371},
	url = {https://www.sciencedirect.com/science/article/pii/S0378437101001443},
	doi = {10.1016/S0378-4371(01)00144-3},
	abstract = {We examine the detrended fluctuation analysis (DFA), which is a well-established method for the detection of long-range correlations in time series. We show that deviations from scaling which appear at small time scales become stronger in higher orders of DFA, and suggest a modified DFA method to remove them. The improvement is necessary especially for short records that are affected by non-stationarities. Furthermore, we describe how crossovers in the correlation behavior can be detected reliably and determined quantitatively and show how several types of trends in the data affect the different orders of DFA.},
	language = {en},
	number = {3},
	urldate = {2023-05-26},
	journal = {Physica A: Statistical Mechanics and its Applications},
	author = {Kantelhardt, Jan W and Koscielny-Bunde, Eva and Rego, Henio H. A and Havlin, Shlomo and Bunde, Armin},
	month = jun,
	year = {2001},
	keywords = {Detrending, Long-range correlations, Time-series analysis},
	pages = {441--454},
}


@article{gao_denoising_2010,
	title = {Denoising {Nonlinear} {Time} {Series} by {Adaptive} {Filtering} and {Wavelet} {Shrinkage}: {A} {Comparison}},
	volume = {17},
	issn = {1070-9908, 1558-2361},
	shorttitle = {Denoising {Nonlinear} {Time} {Series} by {Adaptive} {Filtering} and {Wavelet} {Shrinkage}},
	url = {http://ieeexplore.ieee.org/document/5345722/},
	doi = {10.1109/LSP.2009.2037773},
	number = {3},
	urldate = {2023-05-26},
	journal = {IEEE Signal Processing Letters},
	author = {{Jianbo Gao} and Sultan, H. and {Jing Hu} and {Wen-Wen Tung}},
	month = mar,
	year = {2010},
	pages = {237--240},
}


@article{lindfors_africa_1988,
	title = {Africa and the {Nobel} {Prize}},
	volume = {62},
	issn = {0196-3570},
	url = {https://www.jstor.org/stable/40143532},
	doi = {10.2307/40143532},
	number = {2},
	urldate = {2023-05-26},
	journal = {World Literature Today},
	author = {Lindfors, Bernth},
	year = {1988},
	note = {Publisher: Board of Regents of the University of Oklahoma},
	pages = {222--224},
	file = {Full Text PDF:/Users/au324704/Zotero/storage/FBJLR8E5/Lindfors - 1988 - Africa and the Nobel Prize.pdf:application/pdf},
}


@article{mcdonough_fractal_2023,
	title = {Fractal patterns in music},
	volume = {170},
	issn = {09600779},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0960077923002163},
	doi = {10.1016/j.chaos.2023.113315},
	language = {en},
	urldate = {2023-05-26},
	journal = {Chaos, Solitons \& Fractals},
	author = {McDonough, John and Herczyński, Andrzej},
	month = may,
	year = {2023},
	pages = {113315},
}

@inproceedings{mohammad-2011-upon,
    title = "From Once Upon a Time to Happily Ever After: Tracking Emotions in Novels and Fairy Tales",
    author = "Mohammad, Saif",
    booktitle = "Proceedings of the 5th {ACL}-{HLT} Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities",
    month = jun,
    year = "2011",
    address = "Portland, OR, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W11-1514",
    pages = "105--114",
}


@article{brachmann_computational_2017,
	title = {Computational and {Experimental} {Approaches} to {Visual} {Aesthetics}},
	volume = {11},
	issn = {1662-5188},
	url = {http://journal.frontiersin.org/article/10.3389/fncom.2017.00102/full},
	doi = {10.3389/fncom.2017.00102},
	abstract = {Aesthetics has been the subject of long-standing debates by philosophers and psychologists alike. In psychology, it is generally agreed that aesthetic experience results from an interaction between perception, cognition, and emotion. By experimental means, this triad has been studied in the ﬁeld of experimental aesthetics, which aims to gain a better understanding of how aesthetic experience relates to fundamental principles of human visual perception and brain processes. Recently, researchers in computer vision have also gained interest in the topic, giving rise to the ﬁeld of computational aesthetics. With computing hardware and methodology developing at a high pace, the modeling of perceptually relevant aspect of aesthetic stimuli has a huge potential. In this review, we present an overview of recent developments in computational aesthetics and how they relate to experimental studies. In the ﬁrst part, we cover topics such as the prediction of ratings, style and artist identiﬁcation as well as computational methods in art history, such as the detection of inﬂuences among artists or forgeries. We also describe currently used computational algorithms, such as classiﬁers and deep neural networks. In the second part, we summarize results from the ﬁeld of experimental aesthetics and cover several isolated image properties that are believed to have a effect on the aesthetic appeal of visual stimuli. Their relation to each other and to ﬁndings from computational aesthetics are discussed. Moreover, we compare the strategies in the two ﬁelds of research and suggest that both ﬁelds would greatly proﬁt from a joined research effort. We hope to encourage researchers from both disciplines to work more closely together in order to understand visual aesthetics from an integrated point of view.},
	language = {en},
	urldate = {2022-09-22},
	journal = {Frontiers in Computational Neuroscience},
	author = {Brachmann, Anselm and Redies, Christoph},
	month = nov,
	year = {2017},
	pages = {102}
}

@article{riddell_readers_2018,
	title = {Readers and their roles: {Evidence} from readers of contemporary fiction in the {Netherlands}},
	volume = {13},
	issn = {1932-6203},
	shorttitle = {Readers and their roles},
	url = {https://dx.plos.org/10.1371/journal.pone.0201157},
	doi = {10.1371/journal.pone.0201157},
	language = {en},
	number = {7},
	urldate = {2022-09-15},
	journal = {PLOS ONE},
	author = {Riddell, Allen and van Dalen-Oskam, Karina},
	editor = {Wray, K. Brad},
	month = jul,
	year = {2018},
	pages = {e0201157},
	file = {Riddell and van Dalen-Oskam - 2018 - Readers and their roles Evidence from readers of .pdf:/Users/au324704/Zotero/storage/EUZ95BRZ/Riddell and van Dalen-Oskam - 2018 - Readers and their roles Evidence from readers of .pdf:application/pdf},
}

@article{degaetano2022toward,
  title={Toward an optimal code for communication: The case of scientific English},
  author={Degaetano-Ortlieb, Stefania and Teich, Elke},
  journal={Corpus Linguistics and Linguistic Theory},
  volume={18},
  number={1},
  pages={175--207},
  year={2022},
  publisher={De Gruyter}
}

@article{teich2021less,
  title={Less is more/more diverse: on the communicative utility of linguistic conventionalization},
  author={Teich, Elke and Fankhauser, Peter and Degaetano-Ortlieb, Stefania and Bizzoni, Yuri},
  journal={Frontiers in Communication},
  volume={5},
  pages={620275},
  year={2021},
  publisher={Frontiers Media SA}
}

@book{archer_bestseller_2017,
	address = {London},
	title = {The bestseller code},
	isbn = {978-0-14-198248-9},
	abstract = {Ask most people about massive success in the world of fiction, and you’ll typically hear that it’s a game of hazy crystal balls. The sales figures of E. L. James or Dan Brown seem to be freakish―random occurrences in an unknowable market. But what if there were an algorithm that could reveal a secret DNA of bestsellers, regardless of their genre? What if it knew, just from analyzing the words alone, not just why genre writers like John Grisham and Danielle Steel belong on the lists, but also that authors such as Junot Diaz, Jodi Picoult, and Donna Tartt had telltale signs of success all over their pages? Thanks to Jodie Archer and Matthew Jockers, the algorithm exists, the code has been cracked, and the results bring fresh new insights into how fiction works and why we read. The Bestseller Code offers a new theory for why Fifty Shades of Grey sold so well. It sheds light on the current craze for dark heroines. It reveals which themes tend to sell best. And all with fascinating supporting data taken from a five-year study of twenty thousand novels. Then there is the hunt for "the one"―the paradigmatic example of bestselling writing according to a computer's analysis of thousands of points of data. The result is surprising, a bit ironic, and delightfully unorthodox. This book explains groundbreaking text-mining research in accessible terms and offers a new perspective on the New York Times bestseller list. It's a big-idea book about the relationship between creativity and technology that will be provocative to anyone interested in how analytics have already transformed the worlds of finance, medicine, and sports. But at heart it is a celebration of books for readers and writers―a compelling investigation into how successful writing works, and a fresh take on our intellectual and emotional response to stories},
	language = {eng},
	publisher = {Penguin books},
	author = {Archer, Jodie and Jockers, Matthew Lee},
	year = {2017},
}



@misc{annieswafford_problems_2015,
	title = {Problems with the {Syuzhet} {Package}},
	url = {https://annieswafford.wordpress.com/2015/03/02/syuzhet/},
	language = {en},
	urldate = {2022-09-08},
    author = {Swafford, Annie},
	journal = {Anglophile in Academia:  Annie Swafford's Blog},
	author = {Swafford, Annie},
	month = mar,
	year = {2015},
}



@inproceedings{kim_investigating_2017,
	address = {Vancouver, Canada},
	title = {Investigating the Relationship between Literary Genres and Emotional Plot Development},
	url = {https://aclanthology.org/W17-2203},
	abstract = {Literary genres are commonly viewed as being defined in terms of content and stylistic features. In this paper, we focus on one particular class of lexical features, namely emotion information, and investigate the hypothesis that emotion-related information correlates with particular genres. Using genre classification as a testbed, we compare a model that computes lexicon-based emotion scores globally for complete stories with a model that tracks emotion arcs through stories on a subset of Project Gutenberg with five genres. Our main findings are: (a), the global emotion model is competitive with a large-vocabulary bag-of-words genre classifier (80\%F1); (b), the emotion arc model shows a lower performance (59 \% F1) but shows complementary behavior to the global model, as indicated by a very good performance of an oracle model (94 \% F1) and an improved performance of an ensemble model (84 \% F1); (c), genres differ in the extent to which stories follow the same emotional arcs, with particularly uniform behavior for anger (mystery) and fear (adventures, romance, humor, science fiction).},
	urldate = {2023-05-25},
	booktitle = {Proceedings of the {Joint} {SIGHUM} {Workshop} on {Computational} {Linguistics} for {Cultural} {Heritage}, {Social} {Sciences}, {Humanities} and {Literature}},
	publisher = {Association for Computational Linguistics},
	author = {Kim, Evgeny and Padó, Sebastian and Klinger, Roman},
	month = aug,
	year = {2017},
	pages = {17--26}
}


@inproceedings{liu2019early,
  title={Early prediction of sepsis via smote upsampling and mutual information based downsampling},
  author={Liu, Shiyu and Ong, Ming Lun and Mun, Kar Kin and Yao, Jia and Motani, Mehul},
  booktitle={2019 Computing in Cardiology (CinC)},
  pages={Page--1},
  year={2019},
  organization={IEEE}
}

@inproceedings{rustogi2019swift,
  title={Swift imbalance data classification using SMOTE and extreme learning machine},
  author={Rustogi, Rishabh and Prasad, Ayush},
  booktitle={2019 International Conference on Computational Intelligence in Data Science (ICCIDS)},
  pages={1--6},
  year={2019},
  organization={IEEE}
}
@book{Aho:72,
    author  = {Alfred V. Aho and Jeffrey D. Ullman},
    title   = {The Theory of Parsing, Translation and Compiling},
    year    = "1972",
    volume  = "1",
    publisher = {Prentice-Hall},
    address = {Englewood Cliffs, NJ}
}

@book{APA:83,
    author  = {{American Psychological Association}},
    title   = {Publications Manual},
    year    = "1983",
    publisher = {American Psychological Association},
    address = {Washington, DC}
}

@article{Chandra:81,
	author = {Ashok K. Chandra and Dexter C. Kozen and Larry J. Stockmeyer},
	year = "1981",
	title = {Alternation},
	journal = {Journal of the Association for Computing Machinery},
	volume = "28",
	number = "1",
	pages = "114--133",
	doi = "10.1145/322234.322243",
}

@inproceedings{andrew2007scalable,
  title={Scalable training of {$L_1$}-regularized log-linear models},
  author={Andrew, Galen and Gao, Jianfeng},
  booktitle={Proceedings of the 24th International Conference on Machine Learning},
  pages={33--40},
  year={2007},
  url={https://dl.acm.org/doi/abs/10.1145/1273496.1273501}
}

@book{Gusfield:97,
    author  = {Dan Gusfield},
    title   = {Algorithms on Strings, Trees and Sequences},
    year    = "1997",
    publisher = {Cambridge University Press},
    address = {Cambridge, UK},
    url={https://www.cambridge.org/core/books/algorithms-on-strings-trees-and-sequences/F0B095049C7E6EF5356F0A26686C20D3}
}

@article{rasooli-tetrault-2015,
    author    = {Mohammad Sadegh Rasooli and Joel R. Tetreault},
    title     = {Yara Parser: {A} Fast and Accurate Dependency Parser},
    journal   = {Computing Research Repository},
    volume    = {arXiv:1503.06733},
    year      = {2015},
    url       = {http://arxiv.org/abs/1503.06733},
    note    = {version 2}
}

@article{Ando2005,
	Acmid = {1194905},
	Author = {Ando, Rie Kubota and Zhang, Tong},
	Issn = {1532-4435},
	Issue_Date = {12/1/2005},
	Journal = {Journal of Machine Learning Research},
	Month = dec,
	Numpages = {37},
	Pages = {1817--1853},
	Publisher = {JMLR.org},
	Title = {A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data},
	Volume = {6},
	Year = {2005},
	url={https://www.jmlr.org/papers/volume6/ando05a/ando05a.pdf}
}

@article{ct1965,
  title={An algorithm for the machine calculation of complex {F}ourier series},
  author={Cooley, James W. and Tukey, John W.},
  journal={Mathematics of Computation},
  volume={19},
  number={90},
  pages={297--301},
  year={1965},
  url={https://www.ams.org/journals/mcom/1965-19-090/S0025-5718-1965-0178586-1/S0025-5718-1965-0178586-1.pdf}
}

% Use this file for citations not found in the ACL Anthology (contained in "anthology.bib").
@article{da2019computational,
  title={The computational case against computational literary studies},
  author={Da, Nan Z},
  journal={Critical inquiry},
  volume={45},
  number={3},
  pages={601--639},
  year={2019},
    doi = {10.1086/702594},
  publisher={The University of Chicago Press Chicago, IL}
}
@article{ohman2022computational,
    title = "Computational Exploration of the Origin of Mood in Literary Texts",
    author = {{\"O}hman, Emily  and
      Rossi, Riikka H.},
    booktitle = "Proceedings of the 2nd International Workshop on Natural Language Processing for Digital Humanities",
    month = nov,
    year = "2022",
    address = "Taipei, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.nlp4dh-1.2",
    pages = "8--14",
}

@article{ohman2023affect,
  title={{Affect as Proxy for Mood}},
  author={{\"O}hman, Emily and Rossi, Riikka},
  journal={Journal of Data Mining and Digital Humanities},
  volume={Special Issue: Natural Language Processing for Digital Humanities},
  year={2023}
}



@misc{jockers_that_2015,
	title = {That {Sentimental} {Feeling}},
	url = {https://www.matthewjockers.net/2015/12/20/that-sentimental-feeling/},
	language = {en-US},
	urldate = {2022-09-08},
	journal = {Matthew L. Jockers},
	author = {Jockers, Matthew},
	year = {2015},
	file = {Snapshot:/Users/au324704/Zotero/storage/9MYIPDMN/that-sentimental-feeling.html:text/html},
}


@misc{jockers_revealing_2015,
	title = {Revealing Sentiment and Plot Arcs with the Syuzhet Package},
	url = {https://www.matthewjockers.net/2015/02/02/syuzhet/},
	language = {en-US},
	urldate = {2022-09-07},
	journal = {Matthew L. Jockers},
	author = {Jockers, Matthew},
	year = {2015},
}

@article{dodds_human_2015,
	title = {Human language reveals a universal positivity bias},
	volume = {112},
	doi = {10.1073/pnas.1411678112},
	abstract = {Using human evaluation of 100,000 words spread across 24 corpora in 10 languages diverse in origin and culture, we present evidence of a deep imprint of human sociality in language, observing that (i) the words of natural human language possess a universal positivity bias, (ii) the estimated emotional content of words is consistent between languages under translation, and (iii) this positivity bias is strongly independent of frequency of word use. Alongside these general regularities, we describe interlanguage variations in the emotional spectrum of languages that allow us to rank corpora. We also show how our word evaluations can be used to construct physical-like instruments for both real-time and offline measurement of the emotional content of large-scale texts.},
	number = {8},
	journal = {Proceedings of the National Academy of Sciences},
	author = {Dodds, Peter Sheridan and Clark, Eric M. and Desu, Suma and Frank, Morgan R. and Reagan, Andrew J. and Williams, Jake Ryland and Mitchell, Lewis and Harris, Kameron Decker and Kloumann, Isabel M. and Bagrow, James P. and Megerdoomian, Karine and McMahon, Matthew T. and Tivnan, Brian F. and Danforth, Christopher M.},
	year = {2015},
	pages = {2389--2394},
}


@article{reagan_benchmarking_2016,
	title = {Benchmarking Sentiment Analysis Methods for Large-Scale Texts: A Case for Using Continuum-Scored Words and Word Shift Graphs},
	url = {http://arxiv.org/abs/1512.00531},
	number = {arXiv:1512.00531},
	note = {arXiv},
	author = {Reagan, Andrew J. and Tivnan, Brian and Williams, Jake Ryland and Danforth, Christopher M. and Dodds, Peter Sheridan},
	month = sep,
	year = {2016},
	keywords = {Computer Science - Computation and Language},
}



@article{elkins_can_2019,
	title = {Can Sentiment Analysis Reveal Structure in a Plotless Novel?},
	url = {https://arxiv.org/abs/1910.01441},
	author = {Elkins, Katherine and Chun, Jon},
	year = {2019},
	note = {arXiv},
	keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, Social and Information Networks (cs.SI)},
}


@book{mitchell2008intention,
  title={Intention and Text: Towards an Intentionality of Literary Form},
  author={Mitchell, Kaye},
  year={2008},
  publisher={Bloomsbury Publishing}
}
@inproceedings{guo2009algorithm,
  title={An algorithm for detection of cognitive intentionality in text analysis},
  author={Guo, Yi and Hua, Nan and Shao, Zhiqing},
  booktitle={2009 International Conference on Information Technology and Computer Science},
  volume={2},
  pages={176--179},
  year={2009},
  organization={IEEE}
}
@book{turco2020book,
  title={The book of literary terms: the genres of fiction, drama, nonfiction, literary criticism, and scholarship},
  author={Turco, Lewis},
  year={2020},
  publisher={University of New Mexico Press}
}
@article{ling2006author,
  title={The author, the text, and the reader},
  author={Ling, Clarissa Lee Ai and Lee, Clarissa},
  journal={Literature Study Online},
  volume={15},
  year={2006}
}
@inproceedings{Mayu2017auto,
author = {Kimura, Mayu and Katsurai, Marie},
title = {Automatic Construction of an Emoji Sentiment Lexicon},
year = {2017},
isbn = {9781450349932},
publisher = {Association for Computing Machinery},
abstract = {Emojis have been frequently used to express users' sentiments, emotions, and feelings in text-based communication. To facilitate sentiment analysis of users' posts, an emoji sentiment lexicon with positive, neutral, and negative scores has been recently constructed using manually labeled tweets. However, the number of emojis listed in the lexicon is smaller than that of currently existing emojis, and expanding the lexicon manually requires time and effort to reconstruct the labeled dataset. This paper presents a simple and efficient method for automatically constructing an emoji sentiment lexicon with arbitrary sentiment categories. The proposed method extracts sentiment words from WordNet-Affect and calculates the cooccurrence frequency between the sentiment words and each emoji. Based on the ratio of the number of occurrences of each emoji among the sentiment categories, each emoji is assigned a multidimensional vector whose elements indicate the strength of the corresponding sentiment. In experiments conducted on a collection of tweets, we show a high correlation between the conventional lexicon and our lexicon for three sentiment categories. We also show the results for a new lexicon constructed with additional sentiment categories.},
booktitle = {Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017},
pages = {1033–1036},
numpages = {4},
location = {Sydney, Australia},
series = {ASONAM '17}
}

@article{appel2016hybrid,
  title={A hybrid approach to the sentiment analysis problem at the sentence level},
  author={Appel, Orestes and Chiclana, Francisco and Carter, Jenny and Fujita, Hamido},
  journal={Knowledge-Based Systems},
  volume={108},
  pages={110--124},
  year={2016},
  publisher={Elsevier}
}
@book{Aho:72,
    author  = {Alfred V. Aho and Jeffrey D. Ullman},
    title   = {{The Theory of Parsing, Translation and Compiling}},
    year    = "1972",
    volume  = "1",
    publisher = {Prentice-Hall},
    address = {Englewood Cliffs, NJ}
}
@article{schmidt2021using,
  title={Using Deep Learning for Emotion Analysis of 18th and 19th Century German Plays},
  author={Schmidt, Thomas and Dennerlein, Katrin and Wolff, Christian},
  year={2021},
  publisher={Melusina Press}
}
@book{APA:83,
    author  = {{American Psychological Association}},
    title   = {Publications Manual},
    year    = "1983",
   publisher = {American Psychological Association},
   address = {Washington, DC}
}

@article{ACM:83,
	author = {Association for Computing Machinery},
	year = "1983",
	journal = {Computing Reviews},
	volume = "24",
	number = "11",
	pages = "503--512"
}

@article{Chandra:81,
	author = {Ashok K. Chandra and Dexter C. Kozen and Larry J. Stockmeyer},
	year = {1981},
	title = {Alternation},
	journal = {Journal of the Association for Computing Machinery},
	volume = "28",	
        number = "1",
	pages = "114--133",
}

@inproceedings{andrew2007scalable,
  title={Scalable training of {L1}-regularized log-linear models},
  author={Andrew, Galen and Gao, Jianfeng},
  booktitle={Proceedings of the 24th International Conference on Machine Learning},
  pages={33--40},
  year={2007},
}

@inproceedings{ohman2021lexval,
  title={The Validity of Lexicon-based Emotion Analysis in Interdisciplinary Research},
  author={{\"O}hman, Emily},
  booktitle={{Proceedings of the ICON 2021 workshop NLP4DH}},
  year={2021},
  organization={The Association for Computational Linguistics}
}
@book{lyytikainen2020nordic,
  title={{Nordic Literature of Decadence}},
  author={Lyytik{\"a}inen, Pirjo and Rossi, Riikka and Parente-{\v{C}}apkov{\'a}, Viola and Hinrikus, Mirjam},
  year={2020},
  publisher={Routledge}
}
@article{rossi2017writing,
  title={{Writing Disgust, Writing Realities}},
  author={Rossi, Riikka},
  journal={Sabine Sch{\"o}nfellner, Gudrun Tockner (eds.) Writing Emotions},
  pages={277},
  year={2017}
}
@inproceedings{ohman2020litemo,
author = {Emily {\"O}hman and Rossi, Riikka},
title = {{ Affect and Emotions in Finnish Literature: Combining Qualitative and Quantitative Approaches}},
booktitle={The Language of Emotions: Building and Applying Computational Methods for Emotion Detection for English and Beyond},
year = {2021}
}
@inproceedings{ohman2020emotion,
  title={{Emotion Annotation: Rethinking Emotion Categorization}},
  author={{\"O}hman, Emily},
  booktitle={DHN Post-Proceedings},
  pages={134--144},
  year={2020}
}
@book{Gusfield:97,
    author  = {Dan Gusfield},
    title   = {{Algorithms on Strings, Trees and Sequences}},
    year    = "1997",
    publisher = {Cambridge University Press},
    address = {Cambridge, UK}
}

@inproceedings{borsch2011,
	Address = {Canberra, Australia},
	Author = {Benjamin Borschinger and Mark Johnson},
	Booktitle = {{Proceedings of the Australasian Language Technology Association Workshop 2011}},
	Month = {December},
	Pages = {10--18},
	Title = {{A Particle Filter algorithm for {B}ayesian Wordsegmentation}},
	Year = {2011}}

@article{rasooli-tetrault-2015,
    author    = {Mohammad Sadegh Rasooli and Joel R. Tetreault},
    title     = {{Yara Parser: {A} Fast and Accurate Dependency Parser}},
    journal   = {{Computing Research Repository}},
    volume    = {arXiv:1503.06733},
    year      = {2015},
    url       = {http://arxiv.org/abs/1503.06733},
    note    = {version 2}
}

@article{Ando2005,
	Acmid = {1194905},
	Author = {Ando, Rie Kubota and Zhang, Tong},
	Issn = {1532-4435},
	Issue_Date = {12/1/2005},
	Journal = {Journal of Machine Learning Research},
	Month = dec,
	Numpages = {37},
	Pages = {1817--1853},
	Publisher = {JMLR.org},
	Title = {A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data},
	Volume = {6},
	Year = {2005}}

@InProceedings{P16-1001,
  author =	"Goodman, James
  	 and Vlachos, Andreas
	     and Naradowsky, Jason",
  title =    "Noise reduction and targeted exploration in imitation learning for      Abstract Meaning Representation parsing    ",
  booktitle = 	    "Proceedings of the 54th Annual Meeting of the Association for      Computational Linguistics (Volume 1: Long Papers)    ",
  year =    "2016",
  publisher =	"Association for Computational Linguistics",
  pages =   "1--11",
  location =	"Berlin, Germany",
  doi =    "10.18653/v1/P16-1001",
  url =    "http://aclweb.org/anthology/P16-1001"
}
% Encoding: UTF-8


@InProceedings{conf/sac/AraujoRPB16,
  Title                    = {{An evaluation of machine translation for multilingual sentence-level sentiment analysis.}},
  Author                   = {Ara{\'u}jo, Matheus and dos Reis, J{\'u}lio Cesar and Pereira, Adriano C. M. and Benevenuto, Fabr{\'i}cio},
  Booktitle                = {{SAC}},
  Year                     = {2016},
  Editor                   = {Ossowski, Sascha},
  Pages                    = {1140--1145},
  Publisher                = {ACM},

  Added-at                 = {2016-06-25T00:00:00.000+0200},
  Biburl                   = {http://www.bibsonomy.org/bibtex/2db37655323520deab602582ff47fad91/dblp},
  Crossref                 = {conf/sac/2016},
  Ee                       = {http://doi.acm.org/10.1145/2851613.2851817},
  Interhash                = {19df3151ac6300107a440ef80a34429e},
  Intrahash                = {db37655323520deab602582ff47fad91},
  ISBN                     = {978-1-4503-3739-7},
  Keywords                 = {dblp},
  Owner                    = {esohman},
  Timestamp                = {2016-06-28T12:11:17.000+0200},
  Url                      = {http://dblp.uni-trier.de/db/conf/sac/sac2016.html#AraujoRPB16},
  X-fetchedfrom            = {Bibsonomy}
}
@misc{yang2020semeval2020,
      title={{SemEval-2020 Task 5: Counterfactual recognition}}, 
      author={Xiaoyu Yang and Stephen Obadinma and Huasha Zhao and Qiong Zhang and Stan Matwin and Xiaodan Zhu},
      year={2020},
      eprint={2008.00563},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
@misc{patwa2020semeval2020,
      title={{SemEval-2020 Task 9: Overview of sentiment analysis of code-mixed tweets}}, 
      author={Parth Patwa and Gustavo Aguilar and Sudipta Kar and Suraj Pandey and Srinivas PYKL and Björn Gambäck and Tanmoy Chakraborty and Thamar Solorio and Amitava Das},
      year={2020},
      eprint={2008.04277},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
@inproceedings{socher2013recursive,
  title={{Recursive deep models for semantic compositionality over a sentiment treebank}},
  author={Socher, Richard and Perelygin, Alex and Wu, Jean and Chuang, Jason and Manning, Christopher D and Ng, Andrew Y and Potts, Christopher},
  booktitle={{Proceedings of the 2013 conference on empirical methods in natural language processing}},
  pages={1631--1642},
  year={2013}
}
@article{keith2017hybrid,
title={{A Hybrid Approach for Sentiment Analysis Applied to Paper Reviews}},
author={Keith, Brian and Fuentes, Exequiel and Meneses, Claudio},
year={2017}
}
@article {opinrank,
title={{Opinion-based entity ranking}},
journal = {Information Retrieval},
year = {2011},
keywords = {adhoc multifaceted search, entity oriented search, entity ranking, entity retrieval, product search},
doi = {10.1007/s10791-011-9174-8},
attachments = {[Web Link]},
author = {Kavita Ganesan and ChengXiang Zhai}
}
@inproceedings{he2016ups,
  title={{Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering}},
  author={He, Ruining and McAuley, Julian},
  booktitle={{proceedings of the 25th international conference on world wide web}},
  pages={507--517},
  year={2016}
}
@inproceedings{mcauley2015image,
  title={{Image-based recommendations on styles and substitutes}},
  author={McAuley, Julian and Targett, Christopher and Shi, Qinfeng and Van Den Hengel, Anton},
  booktitle={{Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval}},
  pages={43--52},
  year={2015}
}
@article{loughran2011liability,
  title={{When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks}},
  author={Loughran, Tim and McDonald, Bill},
  journal={The Journal of Finance},
  volume={66},
  number={1},
  pages={35--65},
  year={2011},
  publisher={Wiley Online Library}
}
@inproceedings{chen2014building,
  title={{Building sentiment lexicons for all major languages}},
  author={Chen, Yanqing and Skiena, Steven},
  booktitle={{Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)}},
  pages={383--389},
  year={2014}
}
@inproceedings{bautin2008international,
  title={{International sentiment analysis for news and blogs.}},
  author={Bautin, Mikhail and Vijayarenu, Lohit and Skiena, Steven},
  booktitle={{ICWSM}},
  year={2008}
}
@article{pang2002thumbs,
  title={{Thumbs up? Sentiment classification using machine learning techniques}},
  author={Pang, Bo and Lee, Lillian and Vaithyanathan, Shivakumar},
  journal={arXiv preprint cs/0205070},
  year={2002}
}
@article{young2012affective,
  title={{Affective news: The automated coding of sentiment in political texts}},
  author={Young, Lori and Soroka, Stuart},
  journal={Political Communication},
  volume={29},
  number={2},
  pages={205--231},
  year={2012},
  publisher={Taylor \& Francis}
}
@inproceedings{deng2015mpqa,
  title={{MPQA 3.0: An entity/event-level sentiment corpus}},
  author={Deng, Lingjia and Wiebe, Janyce},
  booktitle={{Proceedings of the 2015 conference of the North American chapter of the association for computational linguistics: human language technologies}},
  pages={1323--1328},
  year={2015}
}
@misc{doccano,
  title={{doccano}: Text Annotation Tool for Human},
  url={https://github.com/doccano/doccano},
  note={Software available from https://github.com/doccano/doccano},
  author={
    Hiroki Nakayama and
    Takahiro Kubo and
    Junya Kamura and
    Yasufumi Taniguchi and
    Xu Liang},
  year={2018},
}
@inproceedings{stenetorp2012brat,
  title={{BRAT: A web-based tool for NLP-assisted text annotation}},
  author={Stenetorp, Pontus and Pyysalo, Sampo and Topi{\'c}, Goran and Ohta, Tomoko and Ananiadou, Sophia and Tsujii, Jun’ichi},
  booktitle={{Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the Association for Computational Linguistics}},
  pages={102--107},
  year={2012}
}

@inbook{hagermark1997overs,
title={{Att översätta från danska – behövs det?}},
booktitle={{Något att orda om}},
year={1997},
author={Hægermark, Marianne},
editors={Blomkvist, Lars Erik & Löfgren, Mats},
publisher={Norstedts},
pages={117-128}
}
@book{gillberg2005fjaril,
  title={{Fj{\"a}ril blir till sommarfluga-problematik i {\"o}vers{\"a}ttning fr{\aa}n danska till svenska}},
  author={Gillberg, Linn},
  year={2005},
  publisher={Malm{\"o} h{\"o}gskola/IMER}
}
@book{norberg2014translating,
  title={{Translating between closely related languages: Johan Falkberget's Christianus Sextus in Swedish}},
  author={Norberg, Ulf and Stachl-Peier, Ursula},
  year={2014},
  publisher={Cambridge Scholars Publishing}
}
@article{Ruokolainen_2019,
   title={{A Finnish news corpus for named entity recognition}},
   volume={54},
   ISSN={1574-0218},
   url={http://dx.doi.org/10.1007/s10579-019-09471-7},
   DOI={10.1007/s10579-019-09471-7},
   number={1},
   journal={Language Resources and Evaluation},
   publisher={Springer Science and Business Media LLC},
   author={Ruokolainen, Teemu and Kauppinen, Pekka and Silfverberg, Miikka and Lindén, Krister},
   year={2019},
   month={Aug},
   pages={247–272}
}

@InProceedings{conf/wassa/Carpuat15,
  Title                    = {{Connotation in Translation.}},
  Author                   = {Carpuat, Marine},
  Booktitle                = {{WASSA@EMNLP}},
  Year                     = {2015},
  Editor                   = {Balahur, Alexandra and van der Goot, Erik and Vossen, Piek and Montoyo, Andr{\'e}s},
  Pages                    = {9--15},
  Publisher                = {The Association for Computer Linguistics},

  Added-at                 = {2016-09-15T00:00:00.000+0200},
  Biburl                   = {http://www.bibsonomy.org/bibtex/256d87484f8bed232c08c2ed15567b8a6/dblp},
  Crossref                 = {conf/wassa/2015},
  Ee                       = {http://aclweb.org/anthology/W/W15/W15-2903.pdf},
  Interhash                = {9d583904b0a5a82b8a0c211c0fc06bd2},
  Intrahash                = {56d87484f8bed232c08c2ed15567b8a6},
  ISBN                     = {978-1-941643-32-7},
  Keywords                 = {dblp},
  Owner                    = {esohman},
  Timestamp                = {2016-09-16T11:33:17.000+0200},
  Url                      = {http://dblp.uni-trier.de/db/conf/wassa/wassa2015.html#Carpuat15},
  X-fetchedfrom            = {Bibsonomy}
}
@InProceedings{conf/icde/Denecke08,
  Title                    = {{Using SentiWordNet for multilingual sentiment analysis.}},
  Author                   = {Denecke, Kerstin},
  Booktitle                = {{ICDE Workshops}},
  Year                     = {2008},
  Pages                    = {507--512},
  Publisher                = {IEEE Computer Society},

  Added-at                 = {2008-05-05T00:00:00.000+0200},
  Biburl                   = {http://www.bibsonomy.org/bibtex/220682fa5b5089b49fd520fd9ee941e4a/dblp},
  Crossref                 = {conf/icde/2008w},
  Ee                       = {http://dx.doi.org/10.1109/ICDEW.2008.4498370},
  Interhash                = {61555d27b90500463d27bf51924e2982},
  Intrahash                = {20682fa5b5089b49fd520fd9ee941e4a},
  ISBN                     = {978-1-4244-2161-9},
  Keywords                 = {dblp},
  Owner                    = {esohman},
  Timestamp                = {2016-04-26T11:51:37.000+0200},
  Url                      = {http://dblp.uni-trier.de/db/conf/icde/icdew2008.html#Denecke08}
}

@InProceedings{conf/nodalida/GinterNLKHVS13,
  Title                    = {{Building a Large Automatically Parsed Corpus of Finnish.}},
  Author                   = {Ginter, Filip and Nyblom, Jenna and Laippala, Veronika and Kohonen, Samuel and Haverinen, Katri and Vihjanen, Simo and Salakoski, Tapio},
  Booktitle                = {{NODALIDA}},
  Year                     = {2013},
  Editor                   = {Oepen, Stephan and Hagen, Kristin and Johannessen, Janne Bondi},
  Pages                    = {291--300},
  Publisher                = {LinkÖping University Electronic Press},
  Series                   = {{LinkÖping Electronic Conference Proceedings}},
  Volume                   = {85},

  Added-at                 = {2014-10-05T00:00:00.000+0200},
  Biburl                   = {http://www.bibsonomy.org/bibtex/203af8f0a800ac717dadb0a124bad4a8c/dblp},
  Crossref                 = {conf/nodalida/2013},
  Ee                       = {http://www.ep.liu.se/ecp_article/index.en.aspx?issue=085 article=026},
  Interhash                = {0c32f99815cff0ea56e67e777e771b3e},
  Intrahash                = {03af8f0a800ac717dadb0a124bad4a8c},
  Keywords                 = {dblp},
  Owner                    = {esohman},
  Timestamp                = {2015-06-18T23:05:35.000+0200},
  Url                      = {http://dblp.uni-trier.de/db/conf/nodalida/nodalida2013.html#GinterNLKHVS13},
  X-fetchedfrom            = {Bibsonomy}
}

@InProceedings{conf/cicling/MunezeroMMS15,
  Title                    = {{EmoTwitter - A Fine-Grained Visualization System for Identifying Enduring Sentiments in Tweets.}},
  Author                   = {Munezero, Myriam and Montero, Calkin Suero and Mozgovoy, Maxim and Sutinen, Erkki},
  Booktitle                = {{CICLing (2)}},
  Year                     = {2015},
  Editor                   = {Gelbukh, Alexander F.},
  Pages                    = {78--91},
  Publisher                = {Springer},
  Series                   = {{Lecture Notes in Computer Science}},
  Volume                   = {9042},

  Added-at                 = {2015-04-10T00:00:00.000+0200},
  Biburl                   = {http://www.bibsonomy.org/bibtex/2678e4253793a3d9b60f1e613855739f0/dblp},
  Crossref                 = {conf/cicling/2015-2},
  Ee                       = {http://dx.doi.org/10.1007/978-3-319-18117-2_6},
  Interhash                = {14bf959d13713e26daab75e87a330cdb},
  Intrahash                = {678e4253793a3d9b60f1e613855739f0},
  ISBN                     = {978-3-319-18116-5},
  Keywords                 = {dblp},
  Owner                    = {esohman},
  Timestamp                = {2015-06-19T02:18:21.000+0200},
  Url                      = {http://dblp.uni-trier.de/db/conf/cicling/cicling2015-2.html#MunezeroMMS15},
  X-fetchedfrom            = {Bibsonomy}
}
@inproceedings{tackstrom2011semi,
  title={{Semi-supervised latent variable models for sentence-level sentiment analysis}},
  author={T{\"a}ckstrÖm, Oscar and McDonald, Ryan},
  booktitle={{Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers-Volume 2}},
  pages={569--574},
  year={2011},
  organization={Association for Computational Linguistics}
}
@inproceedings{wilson2005recognizing,
  title={{Recognizing contextual polarity in phrase-level sentiment analysis}},
  author={Wilson, Theresa and Wiebe, Janyce and Hoffmann, Paul},
  booktitle={{Proceedings of the conference on human language technology and empirical methods in natural language processing}},
  pages={347--354},
  year={2005},
  organization={Association for Computational Linguistics}
}
@article{wilson2009recognizing,
  title={{Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis}},
  author={Wilson, Theresa and Wiebe, Janyce and Hoffmann, Paul},
  journal={Computational linguistics},
  volume={35},
  number={3},
  pages={399--433},
  year={2009},
  publisher={MIT Press}
}

@InProceedings{Andreevskaia:2007:CCK:1621474.1621496,
  Title                    = {{CLaC and CLaC-NB: Knowledge-based and corpus-based approaches to sentiment tagging}},
  Author                   = {Andreevskaia, Alina and Bergler, Sabine},
  Booktitle                = {{Proceedings of the 4th International Workshop on Semantic Evaluations}},
  Year                     = {2007},

  Address                  = {Stroudsburg, PA, USA},
  Pages                    = {117--120},
  Publisher                = {Association for Computational Linguistics},
  Series                   = {SemEval '07},

  Acmid                    = {1621496},
  Bdsk-url-1               = {http://dl.acm.org/citation.cfm?id=1621474.1621496},
  Date-added               = {2017-10-13 12:56:15 +0000},
  Date-modified            = {2017-10-13 12:56:15 +0000},
  Location                 = {Prague, Czech Republic},
  Numpages                 = {4},
  Url                      = {http://dl.acm.org/citation.cfm?id=1621474.1621496}
}

@Article{banea2011multilingual,
  Title                    = {{Multilingual sentiment and subjectivity analysis}},
  Author                   = {Banea, Carmen and Mihalcea, Rada and Wiebe, Janyce},
  Journal                  = {Multilingual natural language processing},
  Year                     = {2011},
  Pages                    = {1--19},
  Volume                   = {6},

  Date-added               = {2017-10-13 11:08:50 +0000},
  Date-modified            = {2017-10-13 11:08:50 +0000}
}

@InProceedings{Bermingham:2009:SIA:1571941.1572127,
  Title                    = {{A Study of inter-annotator agreement for opinion retrieval}},
  Author                   = {Bermingham, Adam and Smeaton, Alan F.},
  Booktitle                = {{Proceedings of the 32Nd International ACM SIGIR Conference on Research and Development in Information Retrieval}},
  Year                     = {2009},

  Address                  = {New York, NY, USA},
  Pages                    = {784--785},
  Publisher                = {ACM},
  Series                   = {SIGIR '09},

  Acmid                    = {1572127},
  Bdsk-url-1               = {http://doi.acm.org/10.1145/1571941.1572127},
  Bdsk-url-2               = {http://dx.doi.org/10.1145/1571941.1572127},
  Date-added               = {2017-10-13 13:01:01 +0000},
  Date-modified            = {2017-10-13 13:01:01 +0000},
  Doi                      = {10.1145/1571941.1572127},
  ISBN                     = {978-1-60558-483-6},
  Keywords                 = {annotation, inter-annotator agreement, opinion retrieval, sentiment analysis},
  Location                 = {Boston, MA, USA},
  Numpages                 = {2},
  Url                      = {http://doi.acm.org/10.1145/1571941.1572127}
}


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@book{boland2013creating,
 title={{Creating an annotated corpus for sentiment analysis of German product reviews}},
 author = {Boland, Katarina and Wira-Alam, Andias and Messerschmidt, Reinhard},
 year = {2013},
 series = {GESIS-Technical Reports},
 pages = {16},
 volume = {2013/05},
 address = {Mannheim},
 publisher = {GESIS - Leibniz-Institut für Sozialwissenschaften},
 issn = {1868-9051},
 urn = {https://nbn-resolving.org/urn:nbn:de:0168-ssoar-339398},
 abstract = {The availability of annotated data is an important prerequisite for the development of machine learning
algorithms for sentiment analysis. However, as manually labeling large datasets is time-consuming
and expensive, few datasets are available and most of them represent a small sample of a very narrow
domain, e.g. movie reviews or reviews of a certain product type. Additionally, many annotated datasets
are available for English texts only. However, the influence of different characteristics of the input
dataset on the performance of algorithms for sentiment analysis remains unclear if only training data
from one specific domain is available or if specific domains are mixed in the test corpus. We therefore
introduce a new dataset for German product reviews of various product types and investigate whether
even small variances in this specific domain (different product types) already exhibit different characteristics,
e.g. with regard to the difficulty of sentiment annotation. The annotation of this corpus lays
the basis for future enhanced annotations of similar corpora and for the extension of our annotations
to corpora of inherently different domains. These will then serve to investigate the influence of different
corpus characteristics on different algorithms for sentiment analysis and as a basis to apply machine
learning methods for sentence-wise sentiment analysis for German texts.},
}

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@InProceedings{brew2010interaction,
  Title                    = {{The interaction between supervised learning and crowdsourcing}},
  Author                   = {Brew, Anthony and Greene, Derek and Cunningham, P{\'a}draig},
  Booktitle                = {{NIPS workshop on computational social science and the wisdom of crowds}},
  Year                     = {2010},

  Date-added               = {2017-10-13 11:07:30 +0000},
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@Book{CastellaniHafferty09,
  Title                    = {{Sociology and Complexity Science: A New Field of Inquiry}},
  Author                   = {Castellani, Brian and Hafferty, Frederic W.},
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@InProceedings{Eisenstein11,
  Title                    = {{Discovering Sociolinguistic Associations with Structured Sparsity}},
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  Timestamp                = {2017.10.15}
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@InProceedings{eryigit2013turksent,
  Title                    = {{TURKSENT: A sentiment annotation tool for social media.}},
  Author                   = {Eryigit, G{\"u}lsen and Cetin, Fatih Samet and Yanik, Meltem and Temel, Tanel and {\c{C}}i{\c{c}}ekli, Ilyas},
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  Year                     = {2013},
  Pages                    = {131--134},

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@InProceedings{Esuli+Sebastiani:06b,
  Title                    = {{SentiWordNet: A publicly available lexical resource for opinion mining}},
  Author                   = {Esuli, Andrea and Sebastiani, Fabrizio},
  Booktitle                = {{Proceedings of Language Resources and Evaluation (LREC)}},
  Year                     = {2006},

  Added-at                 = {2009-03-18T13:40:43.000+0100},
  Biburl                   = {http://www.bibsonomy.org/bibtex/2dcf1c6cd3d0dfecaa0e7b1d93be8dc9a/om},
  Interhash                = {12da884807d2866aa148cde8b036d2c4},
  Intrahash                = {dcf1c6cd3d0dfecaa0e7b1d93be8dc9a},
  Keywords                 = {sentiment mining analysis opinion},
  Owner                    = {esohman},
  Timestamp                = {2009-03-18T13:40:45.000+0100},
  X-fetchedfrom            = {Bibsonomy}
}

@InProceedings{Hansen11,
  Title                    = {{Good Friends, Bad News - Affect and Virality in Twitter}},
  Author                   = {Hansen, Lars Kai and Arvidsson, Adam and Nielsen, Finn {\AA}rup and Colleoni, Elanor and Etter, Michael},
  Booktitle                = {{The 2011 International Workshop on Social Computing, Network, and Services (SocialComNet 2011)}},
  Year                     = {2011},
  Pages                    = {34--43},

  Owner                    = {esohman},
  Timestamp                = {2017.10.15}
}

@Article{journals/lre/HaverinenNVLKMOSG14,
  Title                    = {{Building the essential resources for Finnish: The Turku Dependency Treebank.}},
  Author                   = {Haverinen, Katri and Nyblom, Jenna and Viljanen, Timo and Laippala, Veronika and Kohonen, Samuel and Missil{\"a}, Anna and Ojala, Stina and Salakoski, Tapio and Ginter, Filip},
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  Year                     = {2014},
  Number                   = {3},
  Pages                    = {493--531},
  Volume                   = {48},

  Added-at                 = {2014-09-03T00:00:00.000+0200},
  Biburl                   = {http://www.bibsonomy.org/bibtex/254191f032029d0e2e8f06bdbefba64a9/dblp},
  Ee                       = {http://dx.doi.org/10.1007/s10579-013-9244-1},
  Interhash                = {77eaf1bf1e2710eb71d70d45bec61c69},
  Intrahash                = {54191f032029d0e2e8f06bdbefba64a9},
  Keywords                 = {dblp},
  Owner                    = {esohman},
  Timestamp                = {2015-06-18T08:43:07.000+0200},
  Url                      = {http://dblp.uni-trier.de/db/journals/lre/lre48.html#HaverinenNVLKMOSG14},
  X-fetchedfrom            = {Bibsonomy}
}

@Article{HE2011606,
  Title                    = {{Self-training from labeled features for sentiment analysis}},
  Author                   = {Yulan He and Deyu Zhou},
  Journal                  = {Information Processing \& Management},
  Year                     = {2011},
  Number                   = {4},
  Pages                    = {606 - 616},
  Volume                   = {47},

  Bdsk-url-1               = {http://www.sciencedirect.com/science/article/pii/S0306457310000932},
  Bdsk-url-2               = {https://doi.org/10.1016/j.ipm.2010.11.003},
  Date-added               = {2017-10-13 11:09:40 +0000},
  Date-modified            = {2017-10-13 11:09:40 +0000},
  Doi                      = {https://doi.org/10.1016/j.ipm.2010.11.003},
  ISSN                     = {0306-4573},
  Keywords                 = {Self-learned features},
  Url                      = {http://www.sciencedirect.com/science/article/pii/S0306457310000932}
}
@article{buhlmann2003boosting,
  title={{Boosting with the L 2 loss: Regression and classification}},
  author={B{\"u}hlmann, Peter and Yu, Bin},
  journal={Journal of the American Statistical Association},
  volume={98},
  number={462},
  pages={324--339},
  year={2003},
  publisher={Taylor \& Francis}
}
@InProceedings{Honkela_et_al_2014,
  Title                    = {{Five-Dimensional Sentiment Analysis of Corpora, Documents and Words}},
  Author                   = {Honkela, Timo and Korhonen, Jaakko and Lagus, Krista and Saarinen, Esa},
  Booktitle                = {{Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 10th International Workshop, {WSOM} 2014}},
  Year                     = {2014},
  Pages                    = {209--218},

  Owner                    = {esohman},
  Timestamp                = {2017.10.15}
}

@InProceedings{Hsueh:2009:DQC:1564131.1564137,
  Title                    = {{Data quality from crowdsourcing: A study of annotation selection criteria}},
  Author                   = {Hsueh, Pei-Yun and Melville, Prem and Sindhwani, Vikas},
  Booktitle                = {{Proceedings of the NAACL HLT 2009 Workshop on Active Learning for Natural Language Processing}},
  Year                     = {2009},

  Address                  = {Stroudsburg, PA, USA},
  Pages                    = {27--35},
  Publisher                = {Association for Computational Linguistics},
  Series                   = {HLT '09},

  Acmid                    = {1564137},
  Bdsk-url-1               = {http://dl.acm.org/citation.cfm?id=1564131.1564137},
  Date-added               = {2017-10-13 12:56:53 +0000},
  Date-modified            = {2017-10-13 12:56:53 +0000},
  Location                 = {Boulder, Colorado},
  Numpages                 = {9},
  Url                      = {http://dl.acm.org/citation.cfm?id=1564131.1564137}
}
@inproceedings{konvens:41_scheible12w,
  author = {Christian Scheible and Hinrich Sch{ü}tze},
  title = {Unsupervised sentiment analysis with a simple and fast {Bayesian}
	model using {Part-of-Speech} feature selection},
  booktitle={{Proceedings of KONVENS 2012}},
  year = {2012},
  editor = {Jeremy Jancsary},
  pages = {269-273},
  month = {September},
  publisher = {{Ö}GAI},
  note = {PATHOS 2012 workshop},
  url = {http://www.oegai.at/konvens2012/proceedings/41_scheible12w/}
}
@inproceedings{cheng2017unsupervised,
  title={{Unsupervised sentiment analysis with signed social networks}},
  author={Cheng, Kewei and Li, Jundong and Tang, Jiliang and Liu, Huan},
  booktitle={{Thirty-First AAAI Conference on Artificial Intelligence}},
  year={2017}
}
@article{desai2020detecting,
  title={{Detecting perceived emotions in hurricane disasters}},
  author={Desai, Shrey and Caragea, Cornelia and Li, Junyi Jessy},
  journal={arXiv preprint arXiv:2004.14299},
  year={2020}
}
@article{ragini2018big,
  title={{Big data analytics for disaster response and recovery through sentiment analysis}},
  author={Ragini, J Rexiline and Anand, PM Rubesh and Bhaskar, Vidhyacharan},
  journal={International Journal of Information Management},
  volume={42},
  pages={13--24},
  year={2018},
  publisher={Elsevier}
}
@incollection{beigi2016overview,
  title={{An overview of sentiment analysis in social media and its applications in disaster relief}},
  author={Beigi, Ghazaleh and Hu, Xia and Maciejewski, Ross and Liu, Huan},
  booktitle={{Sentiment analysis and ontology engineering}},
  pages={313--340},
  year={2016},
  publisher={Springer}
}
@inproceedings{felt2020recognizing,
  title={{Recognizing euphemisms and dysphemisms using sentiment analysis}},
  author={Felt, Christian and Riloff, Ellen},
  booktitle={{Proceedings of the Second Workshop on Figurative Language Processing}},
  pages={136--145},
  year={2020}
}
@misc{crabtree_golder_gschwend_indridason_2018,
 title={{It’s not only what you say, it’s also how you say it: The strategic use of campaign sentiment}},
 url={osf.io/preprints/socarxiv/g2sd6},
 DOI={10.31235/osf.io/g2sd6},
 publisher={SocArXiv},
 author={Crabtree, Charles and Golder, Matt and Gschwend, Thomas and Indridason, Indridi H},
 year={2018},
 month={Aug}
}
@article{loper2002nltk,
  title={{NLTK: The natural language toolkit}},
  author={Loper, Edward and Bird, Steven},
  journal={arXiv preprint cs/0205028},
  year={2002}
}
@article{MOHAMMAD2015480,
title = "Sentiment, emotion, purpose, and style in electoral tweets",
journal = "Information Processing \& Management",
volume = "51",
number = "4",
pages = "480 - 499",
year = "2015",
issn = "0306-4573",
doi = "https://doi.org/10.1016/j.ipm.2014.09.003",
url = "http://www.sciencedirect.com/science/article/pii/S0306457314000880",
author = "Saif M. Mohammad and Xiaodan Zhu and Svetlana Kiritchenko and Joel Martin",
keywords = "Sentiment analysis, Tweets, Elections, Emotions, Purpose, Crowdsourcing",
abstract = "Social media is playing a growing role in elections world-wide. Thus, automatically analyzing electoral tweets has applications in understanding how public sentiment is shaped, tracking public sentiment and polarization with respect to candidates and issues, understanding the impact of tweets from various entities, etc. Here, for the first time, we automatically annotate a set of 2012 US presidential election tweets for a number of attributes pertaining to sentiment, emotion, purpose, and style by crowdsourcing. Overall, more than 100,000 crowdsourced responses were obtained for 13 questions on emotions, style, and purpose. Additionally, we show through an analysis of these annotations that purpose, even though correlated with emotions, is significantly different. Finally, we describe how we developed automatic classifiers, using features from state-of-the-art sentiment analysis systems, to predict emotion and purpose labels, respectively, in new unseen tweets. These experiments establish baseline results for automatic systems on this new data."
}


@article{budiharto2018prediction,
  title={{Prediction and analysis of Indonesia Presidential election from Twitter using sentiment analysis}},
  author={Budiharto, Widodo and Meiliana, Meiliana},
  journal={Journal of Big data},
  volume={5},
  number={1},
  pages={51},
  year={2018},
  publisher={Springer}
}
@inproceedings{davidson2017automated,
  title={{Automated hate speech detection and the problem of offensive language}},
  author={Davidson, Thomas and Warmsley, Dana and Macy, Michael and Weber, Ingmar},
  booktitle={{Eleventh international aaai conference on web and social media}},
  year={2017}
}
@article{MANTYLA201816,
title = "The evolution of sentiment analysis — A review of research topics, venues, and top cited papers",
journal = "Computer Science Review",
volume = "27",
pages = "16 - 32",
year = "2018",
issn = "1574-0137",
doi = "https://doi.org/10.1016/j.cosrev.2017.10.002",
author = "Mika V. Mäntylä and Daniel Graziotin and Miikka Kuutila",
keywords = "Sentiment analysis, Opinion mining, Bibliometric study, Text mining, Literature review, Topic modeling, Latent Dirichlet Allocation, Qualitative analysis",
abstract = "Sentiment analysis is one of the fastest growing research areas in computer science, making it challenging to keep track of all the activities in the area. We present a computer-assisted literature review, where we utilize both text mining and qualitative coding, and analyze 6996 papers from Scopus. We find that the roots of sentiment analysis are in the studies on public opinion analysis at the beginning of 20th century and in the text subjectivity analysis performed by the computational linguistics community in 1990’s. However, the outbreak of computer-based sentiment analysis only occurred with the availability of subjective texts on the Web. Consequently, 99\% of the papers have been published after 2004. Sentiment analysis papers are scattered to multiple publication venues, and the combined number of papers in the top-15 venues only represent ca. 30\% of the papers in total. We present the top-20 cited papers from Google Scholar and Scopus and a taxonomy of research topics. In recent years, sentiment analysis has shifted from analyzing online product reviews to social media texts from Twitter and Facebook. Many topics beyond product reviews like stock markets, elections, disasters, medicine, software engineering and cyberbullying extend the utilization of sentiment analysis."
}
@article{DO2019272,
title = "Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review",
journal = "Expert Systems with Applications",
volume = "118",
pages = "272 - 299",
year = "2019",
issn = "0957-4174",
doi = "https://doi.org/10.1016/j.eswa.2018.10.003",
url = "http://www.sciencedirect.com/science/article/pii/S0957417418306456",
author = "Hai Ha Do and PWC Prasad and Angelika Maag and Abeer Alsadoon",
abstract = "The increasing volume of user-generated content on the web has made sentiment analysis an important tool for the extraction of information about the human emotional state. A current research focus for sentiment analysis is the improvement of granularity at aspect level, representing two distinct aims: aspect extraction and sentiment classification of product reviews and sentiment classification of target-dependent tweets. Deep learning approaches have emerged as a prospect for achieving these aims with their ability to capture both syntactic and semantic features of text without requirements for high-level feature engineering, as is the case in earlier methods. In this article, we aim to provide a comparative review of deep learning for aspect-based sentiment analysis to place different approaches in context."
}
@inproceedings{hu2013unsupervised,
  title={{Unsupervised sentiment analysis with emotional signals}},
  author={Hu, Xia and Tang, Jiliang and Gao, Huiji and Liu, Huan},
  booktitle={{Proceedings of the 22nd international conference on World Wide Web}},
  pages={607--618},
  year={2013}
}
@article{henderson2020unstoppable,
  title={{The unstoppable rise of computational linguistics in deep learning}},
  author={Henderson, James},
  journal={{arXiv preprint arXiv:2005.06420}},
  year={2020}
}

@article{bagheri2013latent,
  title={{Latent Dirichlet Markov allocation for sentiment analysis}},
  author={Bagheri, Ayoub and Saraee, MH and De Jong, Franciska and others},
  year={2013},
  publisher={The OR Society, Birmingham, UK}
}
@InProceedings{mohammad2016practical,
  Title                    = {{A practical guide to sentiment annotation: Challenges and solutions.}},
  Author                   = {Mohammad, Saif},
  Booktitle                = {{WASSA@ NAACL-HLT}},
  Year                     = {2016},
  Pages                    = {174--179},

  Date-added               = {2017-10-13 11:04:29 +0000},
  Date-modified            = {2017-10-13 11:04:29 +0000}
}

@Article{Mohammad13,
  Title                    = {{Crowdsourcing a word-emotion association lexicon}},
  Author                   = {Mohammad, Saif M. and Turney, Peter D.},
  Year                     = {2013},
  Number                   = {3},
  Pages                    = {436--465},
  Volume                   = {29},

  Journal                = {{Computational Intelligence}},
  Owner                    = {esohman},
  Timestamp                = {2017.10.15}
}

@InProceedings{MusatThisone:2012:SAU:2392793.2392794,
  Title                    = {{Sentiment Analysis Using a Novel Human Computation Game}},
  Author                   = {Musat, Claudiu-Cristian and Ghasemi, Alireza and Faltings, Boi},
  Booktitle                = {{Proceedings of the 3rd Workshop on the People's Web Meets NLP: Collaboratively Constructed Semantic Resources and Their Applications to NLP}},
  Year                     = {2012},

  Address                  = {Stroudsburg, PA, USA},
  Pages                    = {1--9},
  Publisher                = {Association for Computational Linguistics},

  Acmid                    = {2392794},
  Bdsk-url-1               = {http://dl.acm.org/citation.cfm?id=2392793.2392794},
  Date-added               = {2017-10-13 11:06:34 +0000},
  Date-modified            = {2017-10-13 11:06:34 +0000},
  Location                 = {Jeju, Republic of Korea},
  Numpages                 = {9},
  Url                      = {http://dl.acm.org/citation.cfm?id=2392793.2392794}
}

@article{rossi2022pohjoisia,
  title={Pohjoisia tunteita},
  author={Rossi, Riikka and Lyytik{\"a}inen, Pirjo},
  journal={AVAIN-Kirjallisuudentutkimuksen aikakauslehti},
  volume={19},
  number={1},
  pages={3--9},
  year={2022}
}

@incollection{plutchik1980general,
  title={A general psychoevolutionary theory of emotion},
  author={Plutchik, Robert},
  booktitle={Theories of emotion},
  pages={3--33},
  year={1980},
  publisher={Elsevier}
}


@InProceedings{shin2012annotation,
  Title                    = {{Annotation Scheme for Constructing Sentiment Corpus in Korean.}},
  Author                   = {Shin, Hyopil and Kim, Munhyong and Jang, Hayeon and Cattle, Andrew},
  Booktitle                = {{PACLIC}},
  Year                     = {2012},
  Pages                    = {181--190},

  Date-added               = {2017-10-13 11:08:02 +0000},
  Date-modified            = {2017-10-13 11:08:02 +0000}
}

@Article{shukla2011sentiment,
  Title                    = {{Sentiment analysis of document based on annotation}},
  Author                   = {Shukla, Archana},
  Journal                  = {arXiv preprint arXiv:1111.1648},
  Year                     = {2011},

  Date-added               = {2017-10-13 11:02:12 +0000},
  Date-modified            = {2017-10-13 11:02:12 +0000}
}

@InProceedings{steinberger2011multilingual,
  Title                    = {{Multilingual Entity-Centered Sentiment Analysis Evaluated by Parallel Corpora.}},
  Author                   = {Steinberger, Josef and Lenkova, Polina and Kabadjov, Mijail A and Steinberger, Ralf and Van der Goot, Erik},
  Booktitle                = {{RANLP}},
  Year                     = {2011},
  Pages                    = {770--775},

  Date-added               = {2017-10-13 13:00:04 +0000},
  Date-modified            = {2017-10-13 13:00:04 +0000}
}
@inproceedings{del2017hate,
  title={{Hate me, hate me not: Hate speech detection on facebook}},
  author={Del Vigna, Fabio and Cimino, Andrea and Dell’Orletta, Felice and Petrocchi, Marinella and Tesconi, Maurizio},
  booktitle={{Proceedings of the First Italian Conference on Cybersecurity (ITASEC17)}},
  pages={86--95},
  year={2017}
}
@article{wirz2018persuasion,
  title={{Persuasion through emotion? An experimental test of the emotion-eliciting nature of populist communication}},
  author={Wirz, Dominique S},
  journal={International Journal of Communication},
  volume={12},
  pages={25},
  year={2018}
}
@article{bielik2020application,
  title={{Application of natural language processing to the electoral manifestos of social democratic parties in Central Eastern European countries}},
  author={Bielik, Ivan},
  journal={Politics in Central Europe},
  volume={16},
  number={1},
  pages={259--282},
  year={2020},
  publisher={Sciendo}
}
@article{biessmann2016automating,
  title={{Automating political bias prediction}},
  author={Biessmann, Felix},
  journal={arXiv preprint arXiv:1608.02195},
  year={2016}
}
@article{haselmayer2017sentiment,
  title={{Sentiment analysis of political communication: Combining a dictionary approach with crowdcoding}},
  author={Haselmayer, Martin and Jenny, Marcelo},
  journal={Quality \& quantity},
  volume={51},
  number={6},
  pages={2623--2646},
  year={2017},
  publisher={Springer}
}
@inproceedings{ramteke2016election,
  title={{Election result prediction using Twitter sentiment analysis}},
  author={Ramteke, Jyoti and Shah, Samarth and Godhia, Darshan and Shaikh, Aadil},
  booktitle={{2016 international conference on inventive computation technologies (ICICT)}},
  volume={1},
  pages={1--5},
  year={2016},
  organization={IEEE}
}
@inproceedings{bermingham2011using,
  title={{On using Twitter to monitor political sentiment and predict election results}},
  author={Bermingham, Adam and Smeaton, Alan},
  booktitle={{Proceedings of the Workshop on Sentiment Analysis where AI meets Psychology (SAAIP 2011)}},
  pages={2--10},
  year={2011}
}
@inproceedings{schmidt2017survey,
  title={{A survey on hate speech detection using natural language processing}},
  author={Schmidt, Anna and Wiegand, Michael},
  booktitle={{Proceedings of the Fifth International workshop on natural language processing for social media}},
  pages={1--10},
  year={2017}
}
@InProceedings{udpipe:2016,
  Title                    = {{UDPipe: Trainable pipeline for processing {CoNLL-U} files performing tokenization, morphological analysis, POS tagging and parsing}},
  Author                   = {Straka, Milan and Haji\v{c}, Jan and Strakov{\'a}},
  Booktitle                = {{Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)}},
  Year                     = {2016},

  Address                  = {Paris, France},
  Month                    = {May},
  Publisher                = {European Language Resources Association (ELRA)},

  Date                     = {23-28},
  ISBN                     = {978-2-9517408-9-1},
  Location                 = {Portoro\v{z}, Slovenia},
  Owner                    = {esohman},
  Timestamp                = {2017.10.15}
}

@Article{Tan20082622,
  Title                    = {{An empirical study of sentiment analysis for chinese documents}},
  Author                   = {Tan, Songbo and Zhang, Jin},
  Journal                  = {Expert Systems with Applications},
  Year                     = {2008},
  Number                   = {4},
  Pages                    = {2622--2629},
  Volume                   = {34},

  Abstract                 = {Up to now, there are very few researches conducted on sentiment classification for Chinese documents. In order to remedy this deficiency, this paper presents an empirical study of sentiment categorization on Chinese documents. Four feature selection methods (MI, IG, \{CHI\} and DF) and five learning methods (centroid classifier, K-nearest neighbor, winnow classifier, Naïve Bayes and SVM) are investigated on a Chinese sentiment corpus with a size of 1021 documents. The experimental results indicate that \{IG\} performs the best for sentimental terms selection and \{SVM\} exhibits the best performance for sentiment classification. Furthermore, we found that sentiment classifiers are severely dependent on domains or topics.},
  Doi                      = {10.1016/j.eswa.2007.05.028},
  ISSN                     = {0957-4174},
  Keywords                 = {Sentiment analysis; Information retrieval; Machine learning},
  Owner                    = {esohman},
  Timestamp                = {2017.10.15},
  Url                      = {http://www.sciencedirect.com/science/article/pii/S0957417407001534}
}

@InProceedings{TIEDEMANN12.463,
  Title                    = {{Parallel Data, Tools and Interfaces in OPUS}},
  Author                   = {Tiedemann, Jorg},
  Booktitle                = {{Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}},
  Year                     = {2012},

  Address                  = {Istanbul, Turkey},
  Editor                   = {Chair), Nicoletta Calzolari (Conference and Choukri, Khalid and Declerck, Thierry and Dogan, Mehmet Ugur and Maegaard, Bente and Mariani, Joseph and Odijk, Jan and Piperidis, Stelios},
  Month                    = {may},
  Publisher                = {European Language Resources Association (ELRA)},

  Date                     = {23-25},
  ISBN                     = {978-2-9517408-7-7},
  Language                 = {english},
  Owner                    = {esohman},
  Timestamp                = {2017.10.15}
}

@Article{journals/corr/TrompP14,
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  Journal                  = {CoRR},
  Year                     = {2014},
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  Added-at                 = {2015-01-01T00:00:00.000+0100},
  Biburl                   = {http://www.bibsonomy.org/bibtex/2c6fce032e1edc645d6c2c05b7a57df40/dblp},
  Ee                       = {http://arxiv.org/abs/1412.4682},
  Interhash                = {1d76238994c723e36a83d3883ab58a62},
  Intrahash                = {c6fce032e1edc645d6c2c05b7a57df40},
  Keywords                 = {dblp},
  Owner                    = {esohman},
  Timestamp                = {2015-06-18T04:12:49.000+0200},
  Url                      = {http://dblp.uni-trier.de/db/journals/corr/corr1412.html#TrompP14},
  X-fetchedfrom            = {Bibsonomy}
}

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  Address                  = {Stroudsburg, PA, USA},
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  Owner                    = {esohman},
  Timestamp                = {2017.10.15}
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  Address                  = {Stroudsburg, PA, USA},
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  Acmid                    = {1613783},
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@InProceedings{nowak2010reliable,
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@article{santos2018langemo,
author = {Santos, Diana and Maia, Belinda},
title={{Language, emotion, and the emotions: A computational introduction}},
journal = {Language and Linguistics Compass},
volume = {12},
number = {6},
pages = {e12279},
doi = {10.1111/lnc3.12279},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/lnc3.12279},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/lnc3.12279},
note = {e12279 LNCO-0697.R3},
abstract = {Abstract In this paper, the second in the pair, we discuss how psychologists, linguists, and computer scientists model emotion from a computational perspective. First, we draw some parallels between the formalization of the emotions in the human body and the development of computational systems. Then we look at three influential schools for formalizing emotion in language, and we use a specific example to show that they do not have the same concerns and expressive power. We give some examples of computational resources for emotions, insisting on why the variety of languages and cultures makes it impossible to apply a monolithic universal approach. We then turn to emotion and opinion and briefly overview opinion mining and emotion generation. Instead of describing techniques and technologies and providing quantitative results, we try to present a “big picture” of emotion processing.},
year = {2018}
}
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isbn = {3540252959},
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abstract = {We address the problems of 1/ assessing the confidence of the standard point estimates, precision, recall and F-score, and 2/ comparing the results, in terms of precision, recall and F-score, obtained using two different methods. To do so, we use a probabilistic setting which allows us to obtain posterior distributions on these performance indicators, rather than point estimates. This framework is applied to the case where different methods are run on different datasets from the same source, as well as the standard situation where competing results are obtained on the same data.},
booktitle = {{Proceedings of the 27th European Conference on Advances in Information Retrieval Research}},
pages = {345–359},
numpages = {15},
location = {Santiago de Compostela, Spain},
series = {ECIR'05}
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	title={{Emotion intensities in tweets}},
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	address = {Vancouver, Canada},
	year={2017}
}
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      primaryClass={cs.IR}
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title = {{PyTorch: An imperative style, high-performance deep learning library}},
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booktitle = {Advances in Neural Information Processing Systems 32},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {8024--8035},
year = {2019},
publisher = {Curran Associates, Inc.},
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%From GoEmotions bib
@inproceedings{Ghazi2015DetectingES,
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@InProceedings{Kokensparger:2018:PHW:3159450.3162351,
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@InProceedings{karlin2017dh,
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@Misc{eadh,
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@Misc{project,
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@Misc{eadh2,
  title   = {{Funding: EADH - The European Association for Digital Humanities}},
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@Misc{dhnow,
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@Article{ramsay2012programming,
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@InProceedings{dombrowski2017research,
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@InProceedings{gniady2017dh,
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@InProceedings{tracy2017access,
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@Misc{Acero2011,
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@inproceedings{bermingham2009study,
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@inproceedings{tiedemann2014rediscovering,
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  year={2014}
}
@techreport{yarowsky2001inducing,
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}
@article{eger2018cross,
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}
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}
@inproceedings{ng1999case,
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@article{landis1977measurement,
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@article{doi:10.1111/j.1467-8640.2012.00460.x,
author = {Mohammad, Saif M. and Turney, Peter D.},
title = {{CROWDSOURCING A WORD–EMOTION ASSOCIATION LEXICON}},
journal = {Computational Intelligence},
volume = {29},
number = {3},
pages = {436-465},
keywords = {affect, crowdsourcing, emotion lexicon, emotions, Mechanical Turk, polarity, polarity lexicon, semantic orientation, sentiment analysis, word–emotion associations},
doi = {10.1111/j.1467-8640.2012.00460.x},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-8640.2012.00460.x},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1467-8640.2012.00460.x},
abstract = {Even though considerable attention has been given to the polarity of words (positive and negative) and the creation of large polarity lexicons, research in emotion analysis has had to rely on limited and small emotion lexicons. In this paper, we show how the combined strength and wisdom of the crowds can be used to generate a large, high-quality, word–emotion and word–polarity association lexicon quickly and inexpensively. We enumerate the challenges in emotion annotation in a crowdsourcing scenario and propose solutions to address them. Most notably, in addition to questions about emotions associated with terms, we show how the inclusion of a word choice question can discourage malicious data entry, help to identify instances where the annotator may not be familiar with the target term (allowing us to reject such annotations), and help to obtain annotations at sense level (rather than at word level). We conducted experiments on how to formulate the emotion-annotation questions, and show that asking if a term is associated with an emotion leads to markedly higher interannotator agreement than that obtained by asking if a term evokes an emotion.},
year = {2013}
}


@article{DBLP:journals/corr/MohammadY13,
  author    = {Saif M. Mohammad and
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  url       = {http://arxiv.org/abs/1309.6347},
  archivePrefix = {arXiv},
  eprint    = {1309.6347},
  timestamp = {Mon, 13 Aug 2018 16:46:38 +0200},
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}

@article{DBLP:journals/corr/Mohammad13a,
  author    = {Saif Mohammad},
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  year      = {2013},
  url       = {http://arxiv.org/abs/1309.5909},
  archivePrefix = {arXiv},
  eprint    = {1309.5909},
  timestamp = {Mon, 13 Aug 2018 16:49:01 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/Mohammad13a.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

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  pages={1--17},
  year={2018}
}

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  year={2010}
}

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@inproceedings{gao2016multiscale,
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@article{kanerva2019universal,
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@article{van2018difficult,
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}
@book{54759120130101,
Abstract = {By taking an interdisciplinary approach — with methods drawn from narratology, aesthetics, social psychology, education, and the empirical study of literature — The Art of Sympathy in Fiction will interest scholars in a variety of fields. Its focus is the sympathetic effects of stories, and the possible ways these feelings can contribute to what has been called the “moral imagination.” Part I examines the dynamics of readers'beliefs regarding fictional characters and the influence of those impressions on the emotions that readers experience. The book then turns its attention to sympathy, providing a comprehensive definition and considering the ways in which it operates in life and in literature. Part I concludes with a discussion of the narratological and rhetorical features of fictional narratives that theoretically elicit sympathy in readers. Part II applies these theories to four stories that persuade readers to sympathize with characters who seem unsympathetic. Finally, based on e},
Author = {Sklar, Howard},
ISBN = {9789027233509},
Publisher = {John Benjamins Publishing Company},
Series = {Linguistic Approaches to Literature},
Title = {The Art of Sympathy in Fiction : Forms of Ethical and Emotional Persuasion.},
URL = {http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=547591&site=ehost-live&scope=site},
Year = {2013},
}
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  year={2016}
}

@inproceedings{zhe2002text,
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}

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@INPROCEEDINGS{jaya2018,  
author={R. Jayakrishnan and G. N. Gopal and M. S. Santhikrishna},  booktitle={{{2018 International Conference on Computer Communication and Informatics (ICCCI)}}},   
title={{Multi-Class Emotion Detection and Annotation in Malayalam Novels}},   year={2018}},  
pages={1-5}
}


@inproceedings{francisco2006automated,
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@inproceedings{lin2019secret,
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}

@inproceedings{landt2010sentiment,
  title={{Sentiment Analysis as a Tool for Understanding Fiction}},
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@inbook{hollsten2016,
title = {{Tunnetko kirjallisuutta?: Johdatus suomalaisen kirjallisuuden tutkimukseen tunteiden ja tuntemusten näkökulmasta}},
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year = {2016},
isbn = {978-952-222-742-3},
series = {{Suomalaisen Kirjallisuuden Seuran toimituksia}},
publisher = {{Suomalaisen Kirjallisuuden Seura}},
number = {1425},
editor = {Anna Helle and Anna Hollsten},
booktitle = {{Tunteita ja tuntemuksia suomalaisessa kirjallisuudessa}}
}

@article{kurikka2013algot,
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@article{isomaa2012kirjallisuus,
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  year={2012}
  
}

@article{koljonen2022strategic,
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  year={2022},
  publisher={Springer}
}

@article{maijala2008passion,
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  publisher={{Suomalaisen Kirjallisuuden Seura}}
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@inproceedings{van2018tool,
  title={{Tool criticism: From digital methods to digital methodology}},
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  year={2018}
}
@book{ngai2005ugly,
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@book{richards,
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@book{flatley,
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@article{menninghaus2017distancing,
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  year={2017},
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}
@book{keen2007empathy,
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@book{hogan2011literature,
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@book{rossi2020,
    author = {Rossi, Riikka},
    title = {{Alkukantaisuus ja tunteet — Primitivismi 1900-luvun alun suomalaisessa kirjallisuudessa}},
    keywords = {86 KIRJALLISUUSTIEDE},
    year = {2020},
    language = {suomi},
    series = {Suomalaisen Kirjallisuuden Seuran toimituksia},
    publisher = {Suomalaisen Kirjallisuuden Seura},
    number = {1456}
}
@inproceedings{amano2023analysis,
  title={An Analysis of Representative Works of Japanese L iterature Based on Emotions and Topics},
  author={Amano, Miki and Tsumuraya, Kenshin and Uehara, Minoru and Adachi, Yoshihiro},
  booktitle={Advanced Information Networking and Applications: Proceedings of the 37th International Conference on Advanced Information Networking and Applications (AINA-2023), Volume 2},
  pages={99--112},
  year={2023},
  organization={Springer}
}

@inproceedings{schmidt2018evaluation,
  title={{An Evaluation of Lexicon-based Sentiment Analysis Techniques for the Plays of Gotthold Ephraim Lessing}},
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  pages={139},
  year={2018},
  organization={Association for Computational Linguistics}
}
@article{teodorescu2022frustratingly,
  title={Frustratingly Easy Sentiment Analysis of Text Streams: Generating High-Quality Emotion Arcs Using Emotion Lexicons},
  author={Teodorescu, Daniela and Mohammad, Saif M},
  journal={arXiv preprint arXiv:2210.07381},
  year={2022}
}
@article{mohammad2022best,
  title={Best Practices in the Creation and Use of Emotion Lexicons},
  author={Mohammad, Saif M},
  journal={arXiv preprint arXiv:2210.07206},
  year={2022}
}
@article{wilkens2015digital,
  title={{Digital humanities and its application in the study of literature and culture}},
  author={Wilkens, Matthew},
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  volume={67},
  number={1},
  pages={11--20},
  year={2015},
  publisher={{University of Oregon}}
}
@article{schmidt2016digital,
  title={{Do digital humanists need to understand algorithms?}},
  author={Schmidt, Benjamin M},
  journal={Debates in the Digital Humanities},
  volume={2016},
  pages={53},
  year={2016},
  publisher={{JSTOR}}
}
@misc{lebert2008project,
  title={{Project Gutenberg (1971-2008)}},
  author={Lebert, Marie},
  year={2008},
  publisher={{Project Gutenberg}}
}
@misc{swafford2016messy,
  title={{Messy Data and Faulty Tools}},
  author={Swafford, Joanna},
  year={2016},
  publisher={{JSTOR}}
}
@article{piper2015novel,
  title={{Novel devotions: Conversional reading, computational modeling, and the modern novel}},
  author={Piper, Andrew},
  journal={New Literary History},
  volume={46},
  number={1},
  pages={63--98},
  year={2015},
  publisher={{Johns Hopkins University Press}}
}
@article{swafford2015problems,
	title = {Problems with the {Syuzhet} {Package}},
	url = {https://annieswafford.wordpress.com/2015/03/02/syuzhet/},
	language = {en},
	urldate = {2022-09-08},
    author = {Swafford, Annie},
	journal = {Anglophile in Academia:  Annie Swafford's Blog},
	author = {Swafford, Annie},
	month = mar,
	year = {2015},
}

@article{gunawan2018text,
  title={{Text mining of Scottish post-emergency and training exercise debrief reports}},
  author={Gunawan, Oliver and Aldridge, Timothy},
  year={2018},
  publisher={{}Health and Safety Executive (HSE) under contract to The National Centre for}}
}
@article{aslam2020sentiments,
  title={{Sentiments and emotions evoked by news headlines of coronavirus disease (COVID-19) outbreak}},
  author={Aslam, Faheem and Awan, Tahir Mumtaz and Syed, Jabir Hussain and Kashif, Aisha and Parveen, Mahwish},
  journal={Humanities and Social Sciences Communications},
  volume={7},
  number={1},
  pages={1--9},
  year={2020},
  publisher={{Palgrave}}
}

@article{kim2018survey,
  title={A survey on sentiment and emotion analysis for computational literary studies},
  author={Kim, Evgeny and Klinger, Roman},
  journal={arXiv preprint arXiv:1808.03137},
  year={2018},
  note={arXiv preprint arXiv:1808.03137}
}


@inproceedings{werder2018evolution,
  title={{The evolution of emotional displays in open source software development teams: an individual growth curve analysis}},
  author={Werder, Karl},
  booktitle={{{2018 IEEE/ACM 3rd International Workshop on Emotion Awareness in Software Engineering (SEmotion)}}},
  pages={1--6},
  year={2018},
  organization={{IEEE}}
}
@incollection{lyyti2017written,
    title={{How to Study Emotion Effects in Literature: Written Emotions in Edgar Allan Poe’s “The Fall of the House of Usher}},
    author={Lyytikäinen, Pirjo},
    year={2017},
    booktitle={{Writing Emotions: Theoretical Concepts and Selected Case Studies in Literature}},
    pages={247--64},
    publisher={{Bielefeld: Transcript Verlag}},
    editors={Jandl Ingeborg, Knaller Susanne, Schönfellner Sabine, and Tockner Gudrun}
}
@article{forsman2011virtuaalinen,
  title={Virtuaalinen vanha kirjasuomi},
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  journal={http://www. vvks. info/aanne-\_ja\_muoto-oppi/heittyminen/},
  year={2011}
}
@article{armstrong2014affective,
  title={The Affective Turn in Contemporary Fiction},
  author={Armstrong, Nancy},
  journal={Contemporary Literature},
  volume={55},
  number={3},
  pages={441--465},
  year={2014},
  publisher={University of Wisconsin Press}
}
@article{smith2011postmodernism,
  title={Postmodernism and the affective turn},
  author={Smith, Rachel Greenwald},
  journal={Twentieth Century Literature},
  volume={57},
  number={3/4},
  pages={423--446},
  year={2011},
  publisher={JSTOR}
}

@book{nykros2020,
    author={Nykänen, Elise and Rossi, Riikka},
    title={{Suomalaiset ahdistukset. Kansallinen omakuva ja kielteiset tunteet}},
    publisher={{Kotimaisen kirjallisuuden vuosikirja Joutsen/Svanen -erikoisnumero}},
    year={2020}
}
@book{rossi2007,
    author={Rossi, Riikka},
    title={{Le naturalisme finlandais. Une conception entropique du quotidien}},
    publisher={{Suomalaisen Kirjallisuuden Seura}},
    year={2007}
}
@inproceedings{gniady2017open,
  title={An Open, Reproducible Method for Teaching Text Analysis with R.},
  author={Gniady, Tassie and Wernert, Eric A},
  booktitle={DH},
  year={2017}
}
@Article{virtanen2019multilingual,
  author  = {Virtanen, Antti and Kanerva, Jenna and Ilo, Rami and Luoma, Jouni and Luotolahti, Juhani and Salakoski, Tapio and Ginter, Filip and Pyysalo, Sampo},
  title   = {{Multilingual is not enough: BERT for Finnish}},
  journal = {arXiv preprint arXiv:1912.07076},
  year    = {2019},
}

@InProceedings{rodriguez2019automatic,
  author       = {Rodr{\'\i}guez, Axel and Argueta, Carlos and Chen, Yi-Ling},
  title        = {{Automatic detection of hate speech on facebook using sentiment and emotion analysis}},
  booktitle    = {{2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)}},
  year         = {2019},
  pages        = {169--174},
  organization = {IEEE},
}

@Article{SML-GamingHSDetection,
  author    = {Jesse Haapoja and Salla-Maaria Laaksonen and Airi Lampinen},
  title     = {{Gaming Algorithmic Hate-Speech Detection: Stakes, Parties, and Moves}},
  journal   = {Social Media + Society},
  year      = {2020},
  issn      = {2056-3051},
  doi       = {10.1177/2056305120924778},
  language  = {English},
  publisher = {SAGE Publications Ltd STM},
}




@InProceedings{stanfordNER,
  author    = {Finkel, Jenny Rose and Grenager, Trond and Manning, Christopher},
  title     = {{Incorporating non-local information into information extraction systems by Gibbs sampling}},
  booktitle = {{Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics}},
  year      = {2005},
  series    = {ACL ’05},
  pages     = {363–370},
  address   = {USA},
  publisher = {Association for Computational Linguistics},
  doi       = {10.3115/1219840.1219885},
  location  = {Ann Arbor, Michigan},
  numpages  = {8},
  url       = {https://doi.org/10.3115/1219840.1219885},
}

@Article{huang2019seq2emo,
  author       = {Chenyang Huang and Amine Trabelsi and Xuebin Qin and Nawshad Farruque and Osmar R. Zaïane},
  title        = {{Seq2Emo for multi-label emotion classification based on latent variable chains transformation}},
  journal      = {{arXiv preprint arXiv:1911.02147}},
  year         = {2019},
  eprint       = {1911.02147},
  primaryclass = {cs.CL},
}

@InProceedings{yu-etal-2018-improving,
  author    = {Yu, Jianfei and Marujo, Lu{\'\i}s and Jiang, Jing and Karuturi, Pradeep and Brendel, William},
  title     = {Improving Multi-label Emotion Classification via Sentiment Classification with Dual Attention Transfer Network},
  booktitle = {{Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing}},
  year      = {2018},
  pages     = {1097--1102},
  address   = {Brussels, Belgium},
  month     = oct #{-} # nov,
  publisher = {Association for Computational Linguistics},
  abstract  = {In this paper, we target at improving the performance of multi-label emotion classification with the help of sentiment classification. Specifically, we propose a new transfer learning architecture to divide the sentence representation into two different feature spaces, which are expected to respectively capture the general sentiment words and the other important emotion-specific words via a dual attention mechanism. Experimental results on two benchmark datasets demonstrate the effectiveness of our proposed method.},
  doi       = {10.18653/v1/D18-1137},
  url       = {https://www.aclweb.org/anthology/D18-1137},
}

@Article{jabreel,
  author  = {Jabreel, Mohammed and Moreno, Antonio},
  title   = {{A deep learning-based approach for multi-label emotion classification in tweets}},
  journal = {Applied Sciences},
  year    = {2019},
  volume  = {9},
  pages   = {1123},
  month   = {03},
  doi     = {10.3390/app9061123},
}

@Article{SAMY201861,
  author   = {Ahmed E. Samy and Samhaa R. El-Beltagy and Ehab Hassanien},
  title    = {{A context integrated model for multi-label emotion detection}},
  journal  = {Procedia Computer Science},
  year     = {2018},
  volume   = {142},
  pages    = {61 - 71},
  issn     = {1877-0509},
  note     = {Arabic Computational Linguistics},
  abstract = {This paper explores the impact of taking the environment within which a tweet is made, on the task of analyzing sentiment orientations of tweets produced by people in the same community. The paper proposes C-GRU (Context-aware Gated Recurrent Units), which extracts the contextual information (topics) from tweets and uses them as an extra layer to determine sentiments conveyed by the tweet. The proposed architecture learns direct co-relations between such information and the task’s predication. The multi-modal model combines both outputs learnt (from topics and sentences) by learning the contribution weights of the two sub-modals to the predictions. The evaluation of the proposed model which is carried out by applying it to the SemEval-2018 dataset for Arabic multi-label emotion classification, demonstrate that the model outperforms the highest reported performer on the same dataset.},
  doi      = {https://doi.org/10.1016/j.procs.2018.10.461},
  keywords = {Emotion analysis, Topic/sentiment model, Natural Language processing, Arabic tweets, Deep learning, Transfer learning},
  url      = {http://www.sciencedirect.com/science/article/pii/S1877050918321628},
}

@InProceedings{mohammad-etal-2018-semeval,
  author    = {Mohammad, Saif and Bravo-Marquez, Felipe and Salameh, Mohammad and Kiritchenko, Svetlana},
  title     = {{S}em{E}val-2018 Task 1: Affect in Tweets}},
  booktitle = {{Proceedings of The 12th International Workshop on Semantic Evaluation}},
  year      = {2018},
  pages     = {1--17},
  address   = {New Orleans, Louisiana},
  month     = jun,
  publisher = {Association for Computational Linguistics},
  abstract  = {We present the SemEval-2018 Task 1: Affect in Tweets, which includes an array of subtasks on inferring the affectual state of a person from their tweet. For each task, we created labeled data from English, Arabic, and Spanish tweets. The individual tasks are: 1. emotion intensity regression, 2. emotion intensity ordinal classification, 3. valence (sentiment) regression, 4. valence ordinal classification, and 5. emotion classification. Seventy-five teams (about 200 team members) participated in the shared task. We summarize the methods, resources, and tools used by the participating teams, with a focus on the techniques and resources that are particularly useful. We also analyze systems for consistent bias towards a particular race or gender. The data is made freely available to further improve our understanding of how people convey emotions through language.},
  doi       = {10.18653/v1/S18-1001},
  url       = {https://www.aclweb.org/anthology/S18-1001},
}

@InProceedings{Ghazi2015DetectingES,
  author    = {Diman Ghazi and Diana Inkpen and Stan Szpakowicz},
  title     = {{Detecting Emotion Stimuli in Emotion-Bearing Sentences}},
  booktitle = {{CICLing}},
  year      = {2015},
}



@InProceedings{Mohammad2012EmotionalT,
  author    = {Mohammad, Saif},
  title     = {{{\#}Emotional tweets}},
  booktitle = {{*{SEM} 2012: The First Joint Conference on Lexical and Computational Semantics {--} Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation ({S}em{E}val 2012)}},
  year      = {2012},
  pages     = {246--255},
  address   = {Montr{\'e}al, Canada},
  month     = {7-8 } # jun,
  publisher = {Association for Computational Linguistics},
  url       = {https://www.aclweb.org/anthology/S12-1033},
}

@InProceedings{Schuff2017AnnotationMA,
  author    = {Hendrik Schuff and Jeremy Barnes and Julian Mohme and Sebastian Pad{\'o} and Roman Klinger},
  title     = {{Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus}},
  booktitle = {{WASSA@EMNLP}},
  year      = {2017},
}

@Article{Liu2019DENSAD,
  author  = {Chen Liu and Muhammad Osama and Anderson de Andrade},
  title   = {{DENS: A dataset for multi-class emotion analysis}},
  journal = {{ArXiv}},
  year    = {2019},
  volume  = {abs/1910.11769},
}

@InProceedings{Alm2005EmotionsFT,
  author    = {Alm, Cecilia Ovesdotter and Roth, Dan and Sproat, Richard},
  title     = {{Emotions from text: Machine learning for text-based emotion prediction}},
  booktitle = {{Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing}},
  year      = {2005},
  pages     = {579--586},
  publisher = {Association for Computational Linguistics},
  url       = {https://www.aclweb.org/anthology/H05-1073},
}

@InProceedings{strapparava2007semeval,
  author    = {Strapparava, Carlo and Mihalcea, Rada},
  title     = {{Semeval-2007 task 14: Affective text}},
  booktitle = {{Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)}},
  year      = {2007},
  pages     = {70--74},
}

@InProceedings{maas-EtAl:2011:ACL-HLT2011,
  author    = {Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher},
  title     = {{Learning word vectors for sentiment analysis}},
  booktitle = {{Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies}},
  year      = {2011},
  pages     = {142--150},
  address   = {Portland, Oregon, USA},
  month     = {June},
  publisher = {Association for Computational Linguistics},
  url       = {http://www.aclweb.org/anthology/P11-1015},
}

@InProceedings{blitzer2007biographies,
  author    = {Blitzer, John and Dredze, Mark and Pereira, Fernando},
  title     = {{Biographies, Bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification}},
  booktitle = {{Proceedings of the 45th annual meeting of the association of computational linguistics}},
  year      = {2007},
  pages     = {440--447},
}

@Article{mozetivc2016multilingual,
  author    = {Mozeti{\v{c}}, Igor and Gr{\v{c}}ar, Miha and Smailovi{\'c}, Jasmina},
  title     = {{Multilingual Twitter sentiment classification: The role of human annotators}},
  journal   = {PloS one},
  year      = {2016},
  volume    = {11},
  number    = {5},
  pages     = {e0155036},
  publisher = {Public Library of Science San Francisco, CA USA},
}

@Article{carl2011taxonomy,
  author    = {Carl, Michael and Dragsted, Barbara and Jakobsen, Arnt Lykke},
  title     = {{A taxonomy of human translation styles}},
  journal   = {{Translation journal}},
  year      = {2011},
  volume    = {16},
  number    = {2},
  pages     = {155--168},
  publisher = {Accurapid, NY},
}

@InProceedings{scherrer2020tapaco,
  author    = {Scherrer, Yves},
  title     = {{TaPaCo: A Corpus of Sentential Paraphrases for 73 Languages}},
  booktitle = {{Proceedings of The 12th Language Resources and Evaluation Conference}},
  year      = {2020},
  pages     = {6868--6873},
}

@InProceedings{luoma2020broad,
  author    = {Luoma, Jouni and Oinonen, Miika and PyykÖnen, Maria and Laippala, Veronika and Pyysalo, Sampo},
  title     = {{A Broad-coverage Corpus for Finnish Named Entity Recognition}},
  booktitle = {{Proceedings of The 12th Language Resources and Evaluation Conference}},
  year      = {2020},
  pages     = {4615--4624},
}

@Article{agic2016multilingual,
  author    = {Agi{\'c}, {\v{Z}}eljko and Johannsen, Anders and Plank, Barbara and Alonso, H{\'e}ctor Mart{\'\i}nez and Schluter, Natalie and S{\o}gaard, Anders},
  title     = {{Multilingual projection for parsing truly low-resource languages}},
  journal   = {{Transactions of the Association for Computational Linguistics}},
  year      = {2016},
  volume    = {4},
  pages     = {301--312},
  publisher = {MIT Press},
}

@Article{Scherer1994EvidenceFU,
  author  = {Klaus R. Scherer and Harald G{\"u}nter Wallbott},
  title   = {{Evidence for universality and cultural variation of differential emotion response patterning.}},
  journal = {{Journal of personality and social psychology}},
  year    = {1994},
  volume  = {66 2},
  pages   = {310-28},
}
@inproceedings{strapparava2004wordnet,
  title={{Wordnet Affect: An affective extension of wordnet.}},
  author={Strapparava, Carlo and Valitutti, Alessandro and others},
  booktitle={{LREC}},
  year={2004},
  organization={Citeseer}
}




@article{DBLP:journals/corr/MohammadY13,
  author    = {Saif M. Mohammad and
               Tony Yang},
  title     = {Tracking Sentiment in Mail: How Genders Differ on Emotional Axes},
  journal   = {CoRR},
  volume    = {abs/1309.6347},
  year      = {2013},
  url       = {http://arxiv.org/abs/1309.6347},
  archivePrefix = {arXiv},
  eprint    = {1309.6347},
  timestamp = {Mon, 13 Aug 2018 16:46:38 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/MohammadY13.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/Mohammad13a,
  author    = {Saif Mohammad},
  title     = {From Once Upon a Time to Happily Ever After: Tracking Emotions in
               Novels and Fairy Tales},
  journal   = {CoRR},
  volume    = {abs/1309.5909},
  year      = {2013},
  url       = {http://arxiv.org/abs/1309.5909},
  archivePrefix = {arXiv},
  eprint    = {1309.5909},
  timestamp = {Mon, 13 Aug 2018 16:49:01 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/Mohammad13a.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{mohammad2018semeval,
  title={{Semeval-2018 task 1: Affect in tweets}},
  author={Mohammad, Saif and Bravo-Marquez, Felipe and Salameh, Mohammad and Kiritchenko, Svetlana},
  booktitle={{Proceedings of the 12th international workshop on semantic evaluation}},
  pages={1--17},
  year={2018}
}


@article{jockers2015syuzhet,
  title={{Syuzhet: Extract sentiment and plot arcs from text}},
  author={Jockers, Matthew L},

  year={2015}
}

@article{jockers2015response,
title={{Some thoughts on Annie’s thoughts . . . about Syuzhet}},
 author={Jockers, Matthew L},
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}
@inproceedings{danescu2011mark,
  title={{Mark my words! Linguistic style accommodation in social media}},
  author={Danescu-Niculescu-Mizil, Cristian and Gamon, Michael and Dumais, Susan},
  booktitle={{Proceedings of the 20th international conference on World wide web}},
  pages={745--754},
  year={2011}
}
@article{gonzalez2015signals,
  title={{Signals of public opinion in online communication: A comparison of methods and data sources}},
  author={Gonz{\'a}lez-Bail{\'o}n, Sandra and Paltoglou, Georgios},
  journal={The ANNALS of the American Academy of Political and Social Science},
  volume={659},
  number={1},
  pages={95--107},
  year={2015},
  publisher={SAGE Publications Sage CA: Los Angeles, CA}
}
@article{radford2019language,
  title={{Language models are unsupervised multitask learners}},
  author={Radford, Alec and Wu, Jeffrey and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
  journal={OpenAI Blog},
  volume={1},
  number={8},
  pages={9},
  year={2019}
}
@article{gorman2018russian,
  title={{Russian Formalism}},
  author={Gorman, David},
  journal={A Companion to Literary Theory},
  pages={36--47},
  year={2018},
  publisher={Wiley Online Library}
}
@inproceedings{zhu2015aligning,
  title={{Aligning books and movies: Towards story-like visual explanations by watching movies and reading books}},
  author={Zhu, Yukun and Kiros, Ryan and Zemel, Rich and Salakhutdinov, Ruslan and Urtasun, Raquel and Torralba, Antonio and Fidler, Sanja},
  booktitle={{Proceedings of the IEEE international conference on computer vision}},
  pages={19--27},
  year={2015}
}
@inproceedings{peters-etal-2018-deep,
    title = "Deep Contextualized Word Representations",
    author = "Peters, Matthew  and
      Neumann, Mark  and
      Iyyer, Mohit  and
      Gardner, Matt  and
      Clark, Christopher  and
      Lee, Kenton  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N18-1202",
    doi = "10.18653/v1/N18-1202",
    pages = "2227--2237",
    abstract = "We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis. We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals.",
}
 @article{dettmers_2015, title={{Deep learning in a nutshell: Core concepts}}, url={https://developer.nvidia.com/blog/deep-learning-nutshell-core-concepts/}, journal={{NVIDIA Developer Blog}}, author={Dettmers, Tim}, year={2015}, month={Nov}} 
@article{schwartz2015data,
  title={{Data-driven content analysis of social media: A systematic overview of automated methods}},
  author={Schwartz, H Andrew and Ungar, Lyle H},
  journal={The ANNALS of the American Academy of Political and Social Science},
  volume={659},
  number={1},
  pages={78--94},
  year={2015},
  publisher={Sage Publications Sage CA: Los Angeles, CA}
}
@article{pennebaker2003psychological,
  title={{Psychological aspects of natural language use: Our words, our selves}},
  author={Pennebaker, James W and Mehl, Matthias R and Niederhoffer, Kate G},
  journal={Annual review of psychology},
  volume={54},
  number={1},
  pages={547--577},
  year={2003},
  publisher={Annual Reviews 4139 El Camino Way, PO Box 10139, Palo Alto, CA 94303-0139, USA}
}
@article{su2017analyzing,
  title={{Analyzing public sentiments online: Combining human-and computer-based content analysis}},
  author={Su, Leona Yi-Fan and Cacciatore, Michael A and Liang, Xuan and Brossard, Dominique and Scheufele, Dietram A and Xenos, Michael A},
  journal={Information, Communication \& Society},
  volume={20},
  number={3},
  pages={406--427},
  year={2017},
  publisher={Taylor \& Francis}
}
@article{puschmann2018turning,
  title={{Turning words into consumer preferences: How sentiment analysis is framed in research and the news media}},
  author={Puschmann, Cornelius and Powell, Alison},
  journal={Social Media+ Society},
  volume={4},
  number={3},
  pages={2056305118797724},
  year={2018},
  publisher={SAGE Publications Sage UK: London, England}
}
@inproceedings{gao2016multiscale,
  title={{A multiscale theory for the dynamical evolution of sentiment in novels}},
  author={Gao, Jianbo and Jockers, Matthew L and Laudun, John and Tangherlini, Timothy},
  booktitle={{2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC)}},
  pages={1--4},
  year={2016},
  organization={IEEE}
}

@inproceedings{zehe2016prediction,
  title={{Prediction of happy endings in German novels based on sentiment information}},
  author={Zehe, Albin and Becker, Martin and Hettinger, Lena and Hotho, Andreas and Reger, Isabella and Jannidis, Fotis},
  booktitle={{3rd Workshop on Interactions between Data Mining and Natural Language Processing, Riva del Garda, Italy}},
  pages={9},
  year={2016}
}

@inproceedings{zhe2002text,
  title={{Text-to-emotion engine for real time internet communication}},
  author={Zhe, Xu and Boucouvalas, AC},
  booktitle={{Proceedings of International Symposium on Communication Systems, Networks and DSPs}},
  pages={164--168},
  year={2002},
  organization={Citeseer}
}


@article{jockers2017cran,
  title={{Syuzhet 1.0.4 now on CRAN}},
  author={Jockers, M},
  journal={Matthew L. Jockers},
  year={2017}
}
@INPROCEEDINGS{jaya2018,  
author={R. Jayakrishnan and G. N. Gopal and M. S. Santhikrishna},  booktitle={{2018 International Conference on Computer Communication and Informatics (ICCCI)}},   
title={{Multi-Class Emotion Detection and Annotation in Malayalam Novels}},   year={2018},  
pages={1-5}
}


@inproceedings{francisco2006automated,
  title={{Automated mark up of affective information in english texts}},
  author={Francisco, Virginia and Gerv{\'a}s, Pablo},
  booktitle={{International Conference on Text, Speech and Dialogue}},
  pages={375--382},
  year={2006},
  organization={Springer}
}

@inproceedings{nalisnick2013extracting,
  title={{Extracting sentiment networks from Shakespeare's plays}},
  author={Nalisnick, Eric T and Baird, Henry S},
  booktitle={{2013 12th International Conference on Document Analysis and Recognition}},
  pages={758--762},
  year={2013},
  organization={IEEE}
}

@inproceedings{lin2019secret,
  title={{The Secret to Popular Chinese Web Novels: A Corpus-Driven Study}},
  author={Lin, Yi-Ju and Hsieh, Shu-Kai},
  booktitle={{2nd Conference on Language, Data and Knowledge (LDK 2019)}},
  year={2019},
  organization={Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik}
}

@inproceedings{landt2010sentiment,
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  year={2010}
}
@book{hollsten2016,
title = {{Tunnetko kirjallisuutta?: Johdatus suomalaisen kirjallisuuden tutkimukseen tunteiden ja tuntemusten näkökulmasta}},
author = {Hollsten, Anna and Helle, Anna},
year = {2016},
isbn = {978-952-222-742-3},
series = {{Suomalaisen Kirjallisuuden Seuran toimituksia}},
publisher = {{Suomalaisen Kirjallisuuden Seura}},
number = {1425},

booktitle = {{Tunteita ja tuntemuksia suomalaisessa kirjallisuudessa}}
}

@article{kurikka2013algot,
  title={{Algot Untola ja kirjoittava kone}},
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}

@article{isomaa2012kirjallisuus,
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  year={2012}
}
@article{maijala2008passion,
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}




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@article{chollet2015keras,
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}
@inproceedings{mckinney2010data,
  title={{Data structures for statistical computing in python}},
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  organization={Austin, TX}
}
@book{oliphant2006guide,
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@article{pedregosa2011scikit,
  title={{Scikit-learn: Machine learning in Python}},
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@inproceedings{abadi2016tensorflow,
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}
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  title={{Python programming language.}},
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@misc{team2013r,
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@inproceedings{aulamo2020opustools,
  title={{OpusTools and parallel corpus diagnostics}},
  author={Aulamo, Mikko and Sulubacak, Umut and Virpioja, Sami and Tiedemann, J{\"o}rg},
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  year={2020}
}
@InProceedings{10.1007/978-3-319-67401-8_30,
author="Langlet, Caroline
and Duplessis, Guillaume Dubuisson
and Clavel, Chlo{\'e}",
editor="Beskow, Jonas
and Peters, Christopher
and Castellano, Ginevra
and O'Sullivan, Carol
and Leite, Iolanda
and Kopp, Stefan",
title={{\1}},
booktitle="Intelligent Virtual Agents",
year="2017",
publisher="Springer International Publishing",
address="Cham",
pages="239--242",
abstract="This paper introduces a web-based platform dedicated to the annotation of sentiment-related phenomena in human-agent conversations. The platform focuses on verbal content and deliberately sets aside non-verbal features. It is designed for managing two dialogue features: adjacency pair and conversation progression. Two annotation tasks are considered: (i) the detection of sentiment expressions, (ii) the ranking of user's preferences. These two tasks focus on a set of specific targets. With this demonstration, we aim to introduce this platform to a large scientific audience and to get feedback for future improvements. Our long-term goal is to make the platform available as open-source tool.",
isbn="978-3-319-67401-8"
}


@book{skirbekk2015future,
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}
@techreport{louviere1991best,
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@article{likert1932technique,
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@article{schmidt2016digital,
  title={{Do digital humanists need to understand algorithms?}},
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@misc{lebert2008project,
  title={{Project Gutenberg (1971-2008)}},
  author={Lebert, Marie},
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@article{swafford2016messy,
  title={{Messy data and faulty tools}},
  author={Swafford, Joanna},
  year={2016},
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@article{piper2015novel,
  title={{Novel devotions: Conversional reading, computational modeling, and the modern novel}},
  author={Piper, Andrew},
  journal={New Literary History},
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  pages={63--98},
  year={2015},
  publisher={Johns Hopkins University Press}
}

@article{gunawan2018text,
  title={{Text mining of Scottish post-emergency and training exercise debrief reports}},
  author={Gunawan, Oliver and Aldridge, Timothy},
  year={2018},
  journal={{}Health and Safety Executive (HSE) under contract to The National Centre for}}
}
@article{aslam2020sentiments,
  title={{Sentiments and emotions evoked by news headlines of coronavirus disease (COVID-19) outbreak}},
  author={Aslam, Faheem and Awan, Tahir Mumtaz and Syed, Jabir Hussain and Kashif, Aisha and Parveen, Mahwish},
  journal={Humanities and Social Sciences Communications},
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  number={1},
  pages={1--9},
  year={2020},
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}
@inproceedings{werder2018evolution,
  title={{The evolution of emotional displays in open source software development teams: An individual growth curve analysis}},
  author={Werder, Karl},
  booktitle={{2018 IEEE/ACM 3rd International Workshop on Emotion Awareness in Software Engineering (SEmotion)}},
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  year={2018},
  organization={IEEE}
}
@article{scherer2005emotions,
  title={{What are emotions? And how can they be measured?}},
  author={Scherer, Klaus R},
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@inproceedings{LREC18-AIL,
    author = {Mohammad, Saif M.},
    title = "Word Affect Intensities",
    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
    month = may,
    year = "2018",
    address = "Miyazaki, Japan",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://aclanthology.org/L18-1027",
    pages = {174-183}
}
@book{stone1966general,
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@InProceedings{Hsu2018SocialNLP2E,
  author    = {Chao-Chun Hsu and Lun-Wei Ku},
  title     = {{SocialNLP 2018 EmotionX Challenge Overview: Recognizing Emotions in Dialogues}},
  booktitle = {{SocialNLP@ACL}},
  year      = {2018},
}
@unpublished{mohammad2018presentation,
title={{Word affect intensities}},
author = {Mohammad, Saif},
year = {2018},
note= {Presentation at The International Conference on Language Resources and Evaluation (LREC)},
URL= {https://www.saifmohammad.com/WebDocs/LREC2018-word-emo-talk.pdf},
}
@Article{Delgado2019WhyCK,
  author  = {Delgado, Rosario and Tibau Alberdi, Xavier-Andoni},
  title   = {{Why Cohen’s Kappa should be avoided as performance measure in classification}},
  journal = {PLoS ONE},
  year    = {2019},
  volume  = {14},
  pages   = {1-26},
  doi     = {10.1371/journal.pone.0222916},
}

@misc{acero2011computer,
  title={{Computer-aided natural language annotation}},
  author={Acero, Alejandro and Wang, Ye-Yi and Wong, Leon},
  year={2011},
  month=jul # "~19",
  publisher={Google Patents},
  note={US Patent 7,983,901}
}


@inproceedings{rodriguez2019automatic,
  title={{Automatic detection of hate speech on facebook using sentiment and emotion analysis}},
  author={Rodr{\'\i}guez, Axel and Argueta, Carlos and Chen, Yi-Ling},
  booktitle={{2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)}},
  pages={169--174},
  year={2019},
  organization={IEEE}
}

@article{SML-GamingHSDetection,
title = "Gaming Algorithmic Hate-Speech Detection: Stakes, Parties, and Moves",
author = "Jesse Haapoja and Salla-Maaria Laaksonen and Airi Lampinen",
year = "2020",
doi = "10.1177/2056305120924778",
language = "English",
journal = "Social Media + Society",
issn = "2056-3051",
publisher = "SAGE Publications Ltd STM",
}

@InProceedings{conf/sac/AraujoRPB16,
  Title                    = {An evaluation of machine translation for multilingual sentence-level sentiment analysis.},
  Author                   = {Ara{\'u}jo, Matheus and dos Reis, J{\'u}lio Cesar and Pereira, Adriano C. M. and Benevenuto, Fabr{\'i}cio},
  Booktitle                = {{SAC}},
  Year                     = {2016},
  Editor                   = {Ossowski, Sascha},
  Pages                    = {1140--1145},
  Publisher                = {ACM},

  Added-at                 = {2016-06-25T00:00:00.000+0200},
  Biburl                   = {http://www.bibsonomy.org/bibtex/2db37655323520deab602582ff47fad91/dblp},
  Crossref                 = {conf/sac/2016},
  Ee                       = {http://doi.acm.org/10.1145/2851613.2851817},
  Interhash                = {19df3151ac6300107a440ef80a34429e},
  Intrahash                = {db37655323520deab602582ff47fad91},
  ISBN                     = {978-1-4503-3739-7},
  Keywords                 = {dblp},
  Owner                    = {esohman},
  Timestamp                = {2016-06-28T12:11:17.000+0200},
  Url                      = {http://dblp.uni-trier.de/db/conf/sac/sac2016.html#AraujoRPB16},
  X-fetchedfrom            = {Bibsonomy}
}

@InProceedings{conf/wassa/Carpuat15,
  Title                    = {Connotation in Translation.},
  Author                   = {Carpuat, Marine},
  Booktitle                = {{WASSA@EMNLP}},
  Year                     = {2015},
  Editor                   = {Balahur, Alexandra and van der Goot, Erik and Vossen, Piek and Montoyo, Andr{\'e}s},
  Pages                    = {9--15},
  Publisher                = {The Association for Computer Linguistics},

  Added-at                 = {2016-09-15T00:00:00.000+0200},
  Biburl                   = {http://www.bibsonomy.org/bibtex/256d87484f8bed232c08c2ed15567b8a6/dblp},
  Crossref                 = {conf/wassa/2015},
  Ee                       = {http://aclweb.org/anthology/W/W15/W15-2903.pdf},
  Interhash                = {9d583904b0a5a82b8a0c211c0fc06bd2},
  Intrahash                = {56d87484f8bed232c08c2ed15567b8a6},
  ISBN                     = {978-1-941643-32-7},
  Keywords                 = {dblp},
  Owner                    = {esohman},
  Timestamp                = {2016-09-16T11:33:17.000+0200},
  Url                      = {http://dblp.uni-trier.de/db/conf/wassa/wassa2015.html#Carpuat15},
  X-fetchedfrom            = {Bibsonomy}
}
@inproceedings{tackstrom2011semi,
  title={{Semi-supervised latent variable models for sentence-level sentiment analysis}},
  author={T{\"a}ckstr{\"o}m, Oscar and McDonald, Ryan},
  booktitle={{Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers-Volume 2}},
  pages={569--574},
  year={2011},
  organization={Association for Computational Linguistics}
}


@article{srnka2007words,
  title={{From words to numbers: How to transform qualitative data into meaningful quantitative results}},
  author={Srnka, Katharina J and Koeszegi, Sabine T},
  journal={{Schmalenbach Business Review}},
  volume={59},
  number={1},
  pages={29--57},
  year={2007},
  publisher={Springer}
}



@inproceedings{de2012sentisense,
  title={{SentiSense: An easily scalable concept-based affective lexicon for sentiment analysis.}},
  author={de Albornoz, Jorge Carrillo and Plaza, Laura and Gerv{\'a}s, Pablo}
}
@InProceedings{conf/icde/Denecke08,
  Title                    = {Using SentiWordNet for multilingual sentiment analysis.},
  Author                   = {Denecke, Kerstin},
  Booktitle                = {{ICDE Workshops}},
  Year                     = {2008},
  Pages                    = {507--512},
  Publisher                = {IEEE Computer Society},

  Added-at                 = {2008-05-05T00:00:00.000+0200},
  Biburl                   = {http://www.bibsonomy.org/bibtex/220682fa5b5089b49fd520fd9ee941e4a/dblp},
  Crossref                 = {conf/icde/2008w},
  Ee                       = {http://dx.doi.org/10.1109/ICDEW.2008.4498370},
  Interhash                = {61555d27b90500463d27bf51924e2982},
  Intrahash                = {20682fa5b5089b49fd520fd9ee941e4a},
  ISBN                     = {978-1-4244-2161-9},
  Keywords                 = {dblp},
  Owner                    = {esohman},
  Timestamp                = {2016-04-26T11:51:37.000+0200},
  Url                      = {http://dblp.uni-trier.de/db/conf/icde/icdew2008.html#Denecke08}
}

@InProceedings{conf/nodalida/GinterNLKHVS13,
  Title                    = {Building a Large Automatically Parsed Corpus of Finnish.},
  Author                   = {Ginter, Filip and Nyblom, Jenna and Laippala, Veronika and Kohonen, Samuel and Haverinen, Katri and Vihjanen, Simo and Salakoski, Tapio},
  Booktitle                = {{NODALIDA}},
  Year                     = {2013},
  Editor                   = {Oepen, Stephan and Hagen, Kristin and Johannessen, Janne Bondi},
  Pages                    = {291--300},
  Publisher                = {Link{\"o}ping University Electronic Press},
  Series                   = {{Link{\"o}ping Electronic Conference Proceedings}},
  Volume                   = {85},

  Added-at                 = {2014-10-05T00:00:00.000+0200},
  Biburl                   = {http://www.bibsonomy.org/bibtex/203af8f0a800ac717dadb0a124bad4a8c/dblp},
  Crossref                 = {conf/nodalida/2013},
  Ee                       = {http://www.ep.liu.se/ecp_article/index.en.aspx?issue=085 article=026},
  Interhash                = {0c32f99815cff0ea56e67e777e771b3e},
  Intrahash                = {03af8f0a800ac717dadb0a124bad4a8c},
  Keywords                 = {dblp},
  Owner                    = {esohman},
  Timestamp                = {2015-06-18T23:05:35.000+0200},
  Url                      = {http://dblp.uni-trier.de/db/conf/nodalida/nodalida2013.html#GinterNLKHVS13},
  X-fetchedfrom            = {Bibsonomy}
}

@InProceedings{opus16,
  Title                    = {OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles.},
  Author                   = {Lison, Pierre and Tiedemann, J{\"o}rg},
  Booktitle                = {{LREC}},
  Year                     = {2016},
  Editor                   = {Calzolari, Nicoletta and Choukri, Khalid and Declerck, Thierry and Goggi, Sara and Grobelnik, Marko and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Moreno, Asunci{\'o}n and Odijk, Jan and Piperidis, Stelios},
  Publisher                = {European Language Resources Association (ELRA)},

  Added-at                 = {2016-08-30T00:00:00.000+0200},
  Biburl                   = {http://www.bibsonomy.org/bibtex/25579be3ee174c484a89cae96f42fcd44/dblp},
  Ee                       = {http://www.lrec-conf.org/proceedings/lrec2016/summaries/947.html},
  Interhash                = {bc86e930fe4eb2b98a2abc06d9ea6e82},
  Intrahash                = {5579be3ee174c484a89cae96f42fcd44},
  Keywords                 = {dblp},
  Owner                    = {esohman},
  Timestamp                = {2016-08-31T11:36:54.000+0200},
  Url                      = {http://dblp.uni-trier.de/db/conf/lrec/lrec2016.html#LisonT16},
  X-fetchedfrom            = {Bibsonomy}
}

@InProceedings{conf/cicling/MunezeroMMS15,
  Title                    = {EmoTwitter - A Fine-Grained Visualization System for Identifying Enduring Sentiments in Tweets.},
  Author                   = {Munezero, Myriam and Montero, Calkin Suero and Mozgovoy, Maxim and Sutinen, Erkki},
  Booktitle                = {{CICLing (2)}},
  Year                     = {2015},
  Editor                   = {Gelbukh, Alexander F.},
  Pages                    = {78--91},
  Publisher                = {Springer},
  Series                   = {{Lecture Notes in Computer Science}},
  Volume                   = {9042},

  Added-at                 = {2015-04-10T00:00:00.000+0200},
  Biburl                   = {http://www.bibsonomy.org/bibtex/2678e4253793a3d9b60f1e613855739f0/dblp},
  Crossref                 = {conf/cicling/2015-2},
  Ee                       = {http://dx.doi.org/10.1007/978-3-319-18117-2_6},
  Interhash                = {14bf959d13713e26daab75e87a330cdb},
  Intrahash                = {678e4253793a3d9b60f1e613855739f0},
  ISBN                     = {978-3-319-18116-5},
  Keywords                 = {dblp},
  Owner                    = {esohman},
  Timestamp                = {2015-06-19T02:18:21.000+0200},
  Url                      = {http://dblp.uni-trier.de/db/conf/cicling/cicling2015-2.html#MunezeroMMS15},
  X-fetchedfrom            = {Bibsonomy}
}

@InProceedings{conf/naacl/SalamehMK15,
  Title                    = {Sentiment after Translation: A Case-Study on Arabic Social Media Posts.},
  Author                   = {Salameh, Mohammad and Mohammad, Saif and Kiritchenko, Svetlana},
  Booktitle                = {{HLT-NAACL}},
  Year                     = {2015},
  Editor                   = {Mihalcea, Rada and Chai, Joyce Yue and Sarkar, Anoop},
  Pages                    = {767--777},
  Publisher                = {The Association for Computational Linguistics},

  Added-at                 = {2015-07-11T00:00:00.000+0200},
  Biburl                   = {http://www.bibsonomy.org/bibtex/2cf6f8310631e60e1cf1e8d71cdf169d0/dblp},
  Crossref                 = {conf/naacl/2015},
  Ee                       = {http://aclweb.org/anthology/N/N15/N15-1078.pdf},
  Interhash                = {d7bf5f9d71499abfc34551310e9ee832},
  Intrahash                = {cf6f8310631e60e1cf1e8d71cdf169d0},
  ISBN                     = {978-1-941643-49-5},
  Keywords                 = {dblp},
  Owner                    = {esohman},
  Timestamp                = {2015-07-14T11:40:46.000+0200},
  Url                      = {http://dblp.uni-trier.de/db/conf/naacl/naacl2015.html#SalamehMK15},
  X-fetchedfrom            = {Bibsonomy}
}
@inproceedings{brew2010using,
  title={{Using Crowdsourcing and active learning to track sentiment in online media.}},
  author={Brew, Anthony and Greene, Derek and Cunningham, P{\'a}draig},
  booktitle={{ECAI}},
  pages={145--150},
  year={2010}
}
@article{cohen1960,
  author = {Cohen, J.},
  journal = {Educational and Psychological Measurement},
  keywords = {INFLUENTIAL methods tools},
  number = 1,
  pages = 37,
  title = {{A Coefficient of Agreement for Nominal Scales}},
  volume = {20},
  year = {1960}
}
@inproceedings{poria2017context,
  title={{Context-dependent sentiment analysis in user-generated videos}},
  author={Poria, Soujanya and Cambria, Erik and Hazarika, Devamanyu and Majumder, Navonil and Zadeh, Amir and Morency, Louis-Philippe},
  booktitle={{Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}},
  pages={873--883},
  year={2017}
}
@inproceedings{sabou2014corpus,
  title={{Corpus annotation through crowdsourcing: Towards best practice guidelines.}},
  author={Sabou, Marta and Bontcheva, Kalina and Derczynski, Leon and Scharl, Arno},
  booktitle={{LREC}},
  pages={859--866},
  year={2014}
}

@article{wang2013perspectives,
  title={{Perspectives on crowdsourcing annotations for natural language processing}},
  author={Wang, Aobo and Hoang, Cong Duy Vu and Kan, Min-Yen},
  journal={{Language resources and evaluation}},
  volume={47},
  number={1},
  pages={9--31},
  year={2013},
  publisher={Springer}
}

@InProceedings{Bermingham:2009:SIA:1571941.1572127,
  Title                    = {A study of inter-annotator agreement for opinion retrieval},
  Author                   = {Bermingham, Adam and Smeaton, Alan F.},
  Booktitle                = {Proceedings of the 32Nd International ACM SIGIR Conference on Research and Development in Information Retrieval},
  Year                     = {2009},

  Address                  = {New York, NY, USA},
  Pages                    = {784--785},
  Publisher                = {ACM},
  Series                   = {SIGIR '09},

  Acmid                    = {1572127},
  Bdsk-url-1               = {http://doi.acm.org/10.1145/1571941.1572127},
  Bdsk-url-2               = {http://dx.doi.org/10.1145/1571941.1572127},
  Date-added               = {2017-10-13 13:01:01 +0000},
  Date-modified            = {2017-10-13 13:01:01 +0000},
  Doi                      = {10.1145/1571941.1572127},
  ISBN                     = {978-1-60558-483-6},
  Keywords                 = {annotation, inter-annotator agreement, opinion retrieval, sentiment analysis},
  Location                 = {Boston, MA, USA},
  Numpages                 = {2},
  Url                      = {http://doi.acm.org/10.1145/1571941.1572127}
}

@Article{journals/tacl/BohnetNBFGH13,
  Title                    = {Joint Morphological and Syntactic Analysis for Richly Inflected Languages.},
  Author                   = {Bohnet, Bernd and Nivre, Joakim and Boguslavsky, Igor and Farkas, Rich{\'a}rd and Ginter, Filip and Hajic, Jan},
  Journal                  = {TACL},
  Year                     = {2013},
  Pages                    = {415--428},
  Volume                   = {1},

  Added-at                 = {2015-05-28T00:00:00.000+0200},
  Biburl                   = {http://www.bibsonomy.org/bibtex/29db78aa19627fb7a8c30e9255ed8728c/dblp},
  Ee                       = {https://tacl2013.cs.columbia.edu/ojs/index.php/tacl/article/view/158},
  Interhash                = {b26b3c55e42b34fa93e82899cbb394a9},
  Intrahash                = {9db78aa19627fb7a8c30e9255ed8728c},
  Keywords                 = {dblp},
  Owner                    = {esohman},
  Timestamp                = {2015-06-18T03:01:13.000+0200},
  Url                      = {http://dblp.uni-trier.de/db/journals/tacl/tacl1.html#BohnetNBFGH13},
  X-fetchedfrom            = {Bibsonomy}
}


@InProceedings{brew2010interaction,
  Title                    = {The interaction between supervised learning and crowdsourcing},
  Author                   = {Brew, Anthony and Greene, Derek and Cunningham, P{\'a}draig},
  Booktitle                = {NIPS workshop on computational social science and the wisdom of crowds},
  Year                     = {2010},

  Date-added               = {2017-10-13 11:07:30 +0000},
  Date-modified            = {2017-10-13 11:07:30 +0000}
}

@Book{CastellaniHafferty09,
  Title                    = {Sociology and Complexity Science: A New Field of Inquiry},
  Author                   = {Castellani, Brian and Hafferty, Frederic W.},
  Publisher                = {Springer},
  Year                     = {2009},

  Owner                    = {esohman},
  Timestamp                = {2017.10.15}
}

@InProceedings{Eisenstein11,
  Title                    = {Discovering Sociolinguistic Associations with Structured Sparsity},
  Author                   = {Eisenstein, Jacob and Smith, Noah A. and Xing, Eric P.},
  Booktitle                = {{ACL}},
  Year                     = {2011},
  Editor                   = {Lin, Dekang and Matsumoto, Yuji and Mihalcea, Rada},
  Pages                    = {1365--1374},
  Publisher                = {Association for Computer Linguistics},

  Owner                    = {esohman},
  Timestamp                = {2017.10.15}
}
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  booktitle={{2016 HONET-ICT}},
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  year={2016},
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  organization={Citeseer}
}

@article{ali2017fuzzy,
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  organization={Springer}
}
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  year={2016},
  organization={IEEE}
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@article{al2015lexicon,
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  number={2},
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  year={2015},
  publisher={Inderscience Publishers (IEL)}
}
@article{khoo2018lexicon,
  title={{Lexicon-based sentiment analysis: Comparative evaluation of six sentiment lexicons}},
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  year={2018},
  publisher={SAGE Publications Sage UK: London, England}
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@article{jurek2015improved,
  title={{Improved lexicon-based sentiment analysis for social media analytics}},
  author={Jurek, Anna and Mulvenna, Maurice D and Bi, Yaxin},
  journal={Security Informatics},
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  publisher={SpringerOpen}
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@article{bandhakavi2017lexicon,
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  pages={133--142},
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  publisher={Elsevier}
}
@article{rout2018model,
  title={{A model for sentiment and emotion analysis of unstructured social media text}},
  author={Rout, Jitendra Kumar and Choo, Kim-Kwang Raymond and Dash, Amiya Kumar and Bakshi, Sambit and Jena, Sanjay Kumar and Williams, Karen L},
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  publisher={Springer}
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@article{kolchyna2015Twitter,
  title={{Twitter sentiment analysis: Lexicon method, machine learning method and their combination}},
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  year={2015}
}
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@misc{libovický2019languageneutral,
      title={{How language-neutral is multilingual BERT?}}, 
      author={Jindřich Libovický and Rudolf Rosa and Alexander Fraser},
      year={2019},
      eprint={1911.03310},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
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  author={Conneau, Alexis and Lample, Guillaume and Rinott, Ruty and Williams, Adina and Bowman, Samuel R and Schwenk, Holger and Stoyanov, Veselin},
  journal={arXiv preprint arXiv:1809.05053},
  year={2018}
}
@article{wu2020languages,
      title={{Are all languages created equal in multilingual BERT?}}, 
      author={Shijie Wu and Mark Dredze},
      year={2020},
      eprint={2005.09093},
      journal={arXiv preprint arXiv:2005.09093},
      primaryClass={cs.CL}
}
@article{wu2019beto,
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  year={2019}
}
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}
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}
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@book{scholkopf2018learning,
  title={{Learning with kernels: Support vector machines, regularization, optimization, and beyond}},
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  title={{Multiclass and binary SVM classification: Implications for training and classification users}},
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@inproceedings{bender-koller-2020-climbing,
    title = "Climbing towards {NLU}: {On} Meaning, Form, and Understanding in the Age of Data",
    author = "Bender, Emily M.  and
      Koller, Alexander",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.463",
    doi = "10.18653/v1/2020.acl-main.463",
    pages = "5185--5198",
    abstract = "The success of the large neural language models on many NLP tasks is exciting. However, we find that these successes sometimes lead to hype in which these models are being described as {``}understanding{''} language or capturing {``}meaning{''}. In this position paper, we argue that a system trained only on form has a priori no way to learn meaning. In keeping with the ACL 2020 theme of {``}Taking Stock of Where We{'}ve Been and Where We{'}re Going{''}, we argue that a clear understanding of the distinction between form and meaning will help guide the field towards better science around natural language understanding.",
}
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}
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  year={2017}
}

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  publisher={Elsevier}
}
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@ARTICLE{subasic,  
author={P. {Subasic} and A. {Huettner}},  
journal={IEEE Transactions on Fuzzy Systems},   
title={{Affect analysis of text using fuzzy semantic typing}},   
year={2001},  
volume={9},  
number={4},  
pages={483-496}
}

@InProceedings{Hansen11,
  Title                    = {Good Friends, Bad News - Affect and Virality in Twitter},
  Author                   = {Hansen, Lars Kai and Arvidsson, Adam and Nielsen, Finn {\AA}rup and Colleoni, Elanor and Etter, Michael},
  Booktitle                = {{The 2011 International Workshop on Social Computing, Network, and Services (SocialComNet 2011)}},
  Year                     = {2011},
  Pages                    = {34--43},

  Owner                    = {esohman},
  Timestamp                = {2017.10.15}
}
@article{araque2019depechemood++,
  title={{Depechemood++: A bilingual emotion lexicon built through simple yet powerful techniques}},
  author={Araque, Oscar and Gatti, Lorenzo and Staiano, Jacopo and Guerini, Marco},
  journal={IEEE transactions on affective computing},
  year={2019},
  publisher={IEEE}
}
@article{ilic2018deep,
  title={{Deep contextualized word representations for detecting sarcasm and irony}},
  author={Ili{\'c}, Suzana and Marrese-Taylor, Edison and Balazs, Jorge A and Matsuo, Yutaka},
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  year={2018}
}
@Article{journals/lre/HaverinenNVLKMOSG14,
  Title                    = {Building the essential resources for Finnish: The Turku Dependency Treebank.},
  Author                   = {Haverinen, Katri and Nyblom, Jenna and Viljanen, Timo and Laippala, Veronika and Kohonen, Samuel and Missil{\"a}, Anna and Ojala, Stina and Salakoski, Tapio and Ginter, Filip},
  Journal                  = {Language Resources and Evaluation},
  Year                     = {2014},
  Number                   = {3},
  Pages                    = {493--531},
  Volume                   = {48},

  Added-at                 = {2014-09-03T00:00:00.000+0200},
  Biburl                   = {http://www.bibsonomy.org/bibtex/254191f032029d0e2e8f06bdbefba64a9/dblp},
  Ee                       = {http://dx.doi.org/10.1007/s10579-013-9244-1},
  Interhash                = {77eaf1bf1e2710eb71d70d45bec61c69},
  Intrahash                = {54191f032029d0e2e8f06bdbefba64a9},
  Keywords                 = {dblp},
  Owner                    = {esohman},
  Timestamp                = {2015-06-18T08:43:07.000+0200},
  Url                      = {http://dblp.uni-trier.de/db/journals/lre/lre48.html#HaverinenNVLKMOSG14},
  X-fetchedfrom            = {Bibsonomy}
}




@Article{journals/jair/MohammadSK16,
  Title                    = {How translation alters sentiment.},
  Author                   = {Mohammad, Saif M. and Salameh, Mohammad and Kiritchenko, Svetlana},
  Journal                  = {J. Artif. Intell. Res. (JAIR)},
  Year                     = {2016},
  Pages                    = {95--130},
  Volume                   = {55},

  Added-at                 = {2016-01-26T00:00:00.000+0100},
  Biburl                   = {http://www.bibsonomy.org/bibtex/2c33a16a403f4c209d11821302588c9d7/dblp},
  Ee                       = {http://dx.doi.org/10.1613/jair.4787},
  Interhash                = {5a7863177a155ad2068308cae6f9a78e},
  Intrahash                = {c33a16a403f4c209d11821302588c9d7},
  Keywords                 = {dblp},
  Owner                    = {esohman},
  Timestamp                = {2016-01-27T11:37:56.000+0100},
  Url                      = {http://dblp.uni-trier.de/db/journals/jair/jair55.html#MohammadSK16},
  X-fetchedfrom            = {Bibsonomy}
}



@InProceedings{MusatThisone:2012:SAU:2392793.2392794,
  Title                    = {Sentiment Analysis Using a Novel Human Computation Game},
  Author                   = {Musat, Claudiu-Cristian and Ghasemi, Alireza and Faltings, Boi},
  Booktitle                = {Proceedings of the 3rd Workshop on the People's Web Meets NLP: Collaboratively Constructed Semantic Resources and Their Applications to NLP},
  Year                     = {2012},

  Address                  = {Stroudsburg, PA, USA},
  Pages                    = {1--9},
  Publisher                = {Association for Computational Linguistics},

  Acmid                    = {2392794},
  Date-added               = {2017-10-13 11:06:34 +0000},
  Date-modified            = {2017-10-13 11:06:34 +0000},
  Location                 = {Jeju, Republic of Korea},
  Numpages                 = {9}
}




@InProceedings{shin2012annotation,
  Title                    = {Annotation Scheme for Constructing Sentiment Corpus in Korean.},
  Author                   = {Shin, Hyopil and Kim, Munhyong and Jang, Hayeon and Cattle, Andrew},
  Booktitle                = {PACLIC},
  Year                     = {2012},
  Pages                    = {181--190},

  Date-added               = {2017-10-13 11:08:02 +0000},
  Date-modified            = {2017-10-13 11:08:02 +0000}
}

@Article{shukla2011sentiment,
  Title                    = {Sentiment analysis of document based on annotation},
  Author                   = {Shukla, Archana},
  Journal                  = {arXiv preprint arXiv:1111.1648},
  Year                     = {2011},

  Date-added               = {2017-10-13 11:02:12 +0000},
  Date-modified            = {2017-10-13 11:02:12 +0000}
}

@InProceedings{steinberger2011multilingual,
  Title                    = {Multilingual Entity-Centered Sentiment Analysis Evaluated by Parallel Corpora.},
  Author                   = {Steinberger, Josef and Lenkova, Polina and Kabadjov, Mijail A and Steinberger, Ralf and Van der Goot, Erik},
  Booktitle                = {RANLP},
  Year                     = {2011},
  Pages                    = {770--775},

  Date-added               = {2017-10-13 13:00:04 +0000},
  Date-modified            = {2017-10-13 13:00:04 +0000}
}



@Article{Tan20082622,
  Title                    = {An empirical study of sentiment analysis for chinese documents},
  Author                   = {Tan, Songbo and Zhang, Jin},
  Journal                  = {Expert Systems with Applications},
  Year                     = {2008},
  Number                   = {4},
  Pages                    = {2622--2629},
  Volume                   = {34},

  Abstract                 = {Up to now, there are very few researches conducted on sentiment classification for Chinese documents. In order to remedy this deficiency, this paper presents an empirical study of sentiment categorization on Chinese documents. Four feature selection methods (MI, IG, \{CHI\} and DF) and five learning methods (centroid classifier, K-nearest neighbor, winnow classifier, Naïve Bayes and SVM) are investigated on a Chinese sentiment corpus with a size of 1021 documents. The experimental results indicate that \{IG\} performs the best for sentimental terms selection and \{SVM\} exhibits the best performance for sentiment classification. Furthermore, we found that sentiment classifiers are severely dependent on domains or topics.},
  ISSN                     = {0957-4174},
  Keywords                 = {Sentiment analysis; Information retrieval; Machine learning},
  Owner                    = {esohman},
  Timestamp                = {2017.10.15}
}



@Article{journals/corr/TrompP14,
  Title                    = {Rule-based Emotion Detection on Social Media: Putting Tweets on Plutchik's Wheel.},
  Author                   = {Tromp, Erik and Pechenizkiy, Mykola},
  Journal                  = {CoRR},
  Year                     = {2014},
  Volume                   = {abs/1412.4682},

  Added-at                 = {2015-01-01T00:00:00.000+0100},
  Biburl                   = {http://www.bibsonomy.org/bibtex/2c6fce032e1edc645d6c2c05b7a57df40/dblp},
  Ee                       = {http://arxiv.org/abs/1412.4682},
  Interhash                = {1d76238994c723e36a83d3883ab58a62},
  Intrahash                = {c6fce032e1edc645d6c2c05b7a57df40},
  Keywords                 = {dblp},
  Owner                    = {esohman},
  Timestamp                = {2015-06-18T04:12:49.000+0200},
  Url                      = {http://dblp.uni-trier.de/db/journals/corr/corr1412.html#TrompP14},
  X-fetchedfrom            = {Bibsonomy}
}

@InProceedings{Wan:2008:UBK:1613715.1613783,
  Title                    = {Using Bilingual Knowledge and Ensemble Techniques for Unsupervised Chinese Sentiment Analysis},
  Author                   = {Wan, Xiaojun},
  Booktitle                = {Proceedings of the Conference on Empirical Methods in Natural Language Processing},
  Year                     = {2008},

  Address                  = {Stroudsburg, PA, USA},
  Pages                    = {553--561},
  Publisher                = {Association for Computational Linguistics},
  Series                   = {{EMNLP '08}},

  Acmid                    = {1613783},
  Location                 = {Honolulu, Hawaii},
  Numpages                 = {9},
  Owner                    = {esohman},
  Timestamp                = {2017.10.15},
  Url                      = {http://dl.acm.org/citation.cfm?id=1613715.1613783}
}




@article{kakkonen2011sentiprofiler,
  title={{SentiProfiler: Creating comparable visual profiles of sentimental content in texts}},
  author={Kakkonen, Tuomo and Kakkonen, Gordana Gali{\'c}},
  journal={{Language Technologies for Digital Humanities and Cultural Heritage}},
  volume={62},
  pages={189--204},
  year={2011}
}



@inproceedings{hamari2013social,
  title={{Social Motivations To Use Gamification: An Empirical Study Of Gamifying Exercise.}},
  author={Hamari, Juho and Koivisto, Jonna},
  booktitle={{ECIS}},
  pages={105},
  year={2013}
}
@article{buhrmester2011amazon,
  title={{Amazon's Mechanical Turk: A new source of inexpensive, yet high-quality, data?}},
  author={Buhrmester, Michael and Kwang, Tracy and Gosling, Samuel D},
  journal={{Perspectives on psychological science}},
  volume={6},
  number={1},
  pages={3--5},
  year={2011},
  publisher={Sage Publications Sage CA: Los Angeles, CA}
}
@inproceedings{greenhill2014playing,
  title={{Playing with science: Gamised aspects of gamification found on the online citizen science project-zooniverse}},
  author={Greenhill, Anita and Holmes, Kate and Lintott, Chris and Simmons, Brooke and Masters, Karen and Cox, Joe and Graham, Gary},
  booktitle={{GAMEON'2014}},
  year={2014},
  organization={EUROSIS}
}
@misc{schell2015pleasure,
  title={{The Pleasure Revolution: Why Games Will Lead the Way, GoogleTechTalks Std. November 2011}},
  author={Schell, J},
  year={2015}
}
@inproceedings{hu2013unsupervised,
  title={{Unsupervised sentiment analysis with emotional signals}},
  author={Hu, Xia and Tang, Jiliang and Gao, Huiji and Liu, Huan},
  booktitle={{Proceedings of the 22nd international conference on World Wide Web}},
  pages={607--618},
  year={2013},
  organization={ACM}
}
@incollection{li2017reflections,
  title={{Reflections on sentiment/opinion analysis}},
  author={Li, Jiwei and Hovy, Eduard},
  booktitle={{A practical guide to sentiment analysis}},
  pages={41--59},
  year={2017},
  publisher={Springer}
}
@article{ehrmann2011building,
  title={{Building a Multilingual Named Entity-Annotated Corpus Using Annotation Projection}},
  author={Ehrmann, Maud and Turchi, Marco and Steinberger, Ralf},
  journal={{RECENT ADVANCES IN}},
  pages={118},
  year={2011}
}
@article{DBLP:journals/corr/abs-1708-03696,
  author    = {Saif M. Mohammad and
               Felipe Bravo{-}Marquez},
  title     = {Emotion intensities in tweets},
  journal   = {CoRR},
  volume    = {abs/1708.03696},
  year      = {2017},
  url       = {http://arxiv.org/abs/1708.03696},
  timestamp = {Tue, 05 Sep 2017 10:03:46 +0200},
  biburl    = {http://dblp.org/rec/bib/journals/corr/abs-1708-03696},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}
@article{groh2012gamification,
  title={{Gamification: State of the art definition and utilization}},
  author={Groh, Fabian},
  journal={{Institute of Media Informatics Ulm University}},
  volume={39},
  year={2012}
}
@inproceedings{deterding2011gamification,
  title={{Gamification. using game-design elements in non-gaming contexts}},
  author={Deterding, Sebastian and Sicart, Miguel and Nacke, Lennart and O'Hara, Kenton and Dixon, Dan},
  booktitle={{CHI'11 extended abstracts on human factors in computing systems}},
  pages={2425--2428},
  year={2011},
  organization={ACM}
}
@article{rasooli2017cross,
  title={{Cross-lingual sentiment transfer with limited resources}},
  author={Rasooli, Mohammad Sadegh and Farra, Noura and Radeva, Axinia and Yu, Tao and McKeown, Kathleen},
  journal={{Machine Translation}},
  pages={1--23},
  year={2017},
  publisher={Springer}
}

@book{Aho:72,
    author  = {Alfred V. Aho and Jeffrey D. Ullman},
    title   = {{The Theory of Parsing, Translation and Compiling}},
    year    = "1972",
    volume  = "1",
    publisher = {Prentice-Hall},
    address = {Englewood Cliffs, NJ}
}

@book{APA:83,
    author  = {{American Psychological Association}},
    title   = {Publications Manual},
    year    = "1983",
   publisher = {American Psychological Association},
   address = {Washington, DC}
}

@article{ACM:83,
	author = {Association for Computing Machinery},
	year = "1983",
	journal = {Computing Reviews},
	volume = "24",
	number = "11",
	pages = "503--512"
}

@article{Chandra:81,
	author = {Ashok K. Chandra and Dexter C. Kozen and Larry J. Stockmeyer},
	year = {1981},
	title={{Alternation}},
	journal = {Journal of the Association for Computing Machinery},
	volume = "28",	
        number = "1",
	pages = "114--133",
}

@inproceedings{andrew2007scalable,
  title={{Scalable training of {L1}-regularized log-linear models}},
  author={Andrew, Galen and Gao, Jianfeng},
  booktitle={{Proceedings of the 24th International Conference on Machine Learning}},
  pages={33--40},
  year={2007},
}

@book{Gusfield:97,
    author  = {Dan Gusfield},
    title   = {Algorithms on Strings, Trees and Sequences},
    year    = "1997",
    publisher = {Cambridge University Press},
    address = {Cambridge, UK}
}

@inproceedings{borsch2011,
	Address = {Canberra, Australia},
	Author = {Benjamin Borschinger and Mark Johnson},
	Booktitle={{Proceedings of the Australasian Language Technology Association Workshop 2011}},
	Month = {December},
	Pages = {10--18},
	title={{A Particle Filter algorithm for {B}ayesian Wordsegmentation}},
	Year = {2011}}

@article{rasooli-tetrault-2015,
    author    = {Mohammad Sadegh Rasooli and Joel R. Tetreault},
    title     = {Yara Parser: {A} Fast and Accurate Dependency Parser},
    journal   = {Computing Research Repository},
    volume    = {arXiv:1503.06733},
    year      = {2015},
    url       = {http://arxiv.org/ abs/1503.06733},
    note    = {version 2}
}

@article{Ando2005,
	Acmid = {1194905},
	Author = {Ando, Rie Kubota and Zhang, Tong},
	Issn = {1532-4435},
	Issue_Date = {12/1/2005},
	Journal = {Journal of Machine Learning Research},
	Month = dec,
	Numpages = {37},
	Pages = {1817--1853},
	Publisher = {JMLR.org},
	title={{A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data}},
	Volume = {6},
	Year = {2005}}

@InProceedings{P16-1001,
  author =	"Goodman, James
  	 and Vlachos, Andreas
	     and Naradowsky, Jason",
  title =    "Noise reduction and targeted exploration in imitation learning for      Abstract Meaning Representation parsing    ",
  booktitle = 	    "Proceedings of the 54th Annual Meeting of the Association for      Computational Linguistics (Volume 1: Long Papers)    ",
  year =    "2016",
  publisher =	"Association for Computational Linguistics",
  pages =   "1--11",
  location =	"Berlin, Germany",
  doi =    "10.18653/v1/P16-1001",
  url =    "http://aclweb.org/ anthology/P16-1001"
}

@InProceedings{C14-1001,
  author =	"Harper, Mary",
  title = 	"Learning from 26 Languages: Program Management and Science in the Babel Program",
  booktitle = 	"Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers",
  year =    "2014",
  publisher =	"Dublin City University and Association for Computational Linguistics",
  pages =   "1",
  location =	"Dublin, Ireland",
  url =    "http://aclweb.org/ anthology/C14-1001"
}


@article{DBLP:journals/coling/Wan11,
  author    = {Xiaojun Wan},
  title     = {{Bilingual Co-Training for Sentiment Classification of Chinese Product
               Reviews}},
  journal   = {Computational Linguistics},
  volume    = {37},
  number    = {3},
  pages     = {587--616},
  year      = {2011},
  note = {Accessed December 6 2017.}
}


@inproceedings{DBLP:conf/naacl/SalamehMK15,
  author    = {Mohammad Salameh and
               Saif Mohammad and
               Svetlana Kiritchenko},
  title     = {{Sentiment after Translation: {A} Case-Study on Arabic Social Media
               Posts}},
  booktitle = {{HLT-NAACL}},
  pages     = {767--777},
  publisher = {The Association for Computational Linguistics},
  year      = {2015},
  note = {Accessed September 29 2017.}
}

@inproceedings{Tang:2014:LBE:2682648.2682816,
 author = {Tang, Xuewei and Wan, Xiaojun},
 title = {{Learning Bilingual Embedding Model for Cross-Language Sentiment Classification}},
 booktitle={{Proceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 02}},
 series = {WI-IAT '14},
 year = {2014},
 isbn = {978-1-4799-4143-8},
 pages = {134--141},
 publisher = {IEEE Computer Society},
 address = {Washington, DC, USA},
 note = {Accessed December 10 2017.}
}

@article{Banea11multilingualsentiment,
    author = {Carmen Banea and Rada Mihalcea and Janyce Wiebe},
    title = {{Multilingual sentiment and subjectivity analysis}},
    year = {2011},
    journal = {Multilingual natural language processing},
    volume = {6},
    pages = {1--19},
    publisher = {Prentice-Hall New York, New York},
    note = {Accessed October 16, 2018.},
}



@inproceedings{Lu:2011:JBS:2002472.2002514,
 author = {Lu, Bin and Tan, Chenhao and Cardie, Claire and Tsou, Benjamin K.},
 title = {{Joint Bilingual Sentiment Classification with Unlabeled Parallel Corpora}},
 booktitle={{Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies}},
 volume ={1},
 series = {HLT '11},
 year = {2011},
 location = {Portland, Oregon},
 pages = {320--330},
 publisher = {Association for Computational Linguistics},
 address = {Stroudsburg, PA, USA},
 note = {Accessed October 11 2017.}
}

@InProceedings{D15-1016,
  author = 	"Jain, Sarthak
		and Batra, Shashank",
  title = 	{{Cross Lingual Sentiment Analysis using Modified BRAE}},
  booktitle = 	"Proceedings of the 2015 Conference on Empirical Methods in Natural      Language Processing    ",
  year = 	"2015",
  publisher = 	"Association for Computational Linguistics",
  pages = 	"159--168",
  location = 	"Lisbon, Portugal",
  note = {Accessed December 5 2017.}
}


@article{creutz,
author = {Creutz, Mathias},
title = {{Open Subtitles Paraphrase Corpus for Six Languages}},
year = {2018},
journal={{arXiv preprint arXiv:1809.06142}},
note = {arXiv:1809.06142 [cs.CL]. Accessed November 9 2018.},
archivePrefix = {arXiv}, 
}

@article{Galar:2011:OEM:1963661.1963815,
 author = {Galar, Mikel and Fern\'{a}ndez, Alberto and Barrenechea, Edurne and Bustince, Humberto and Herrera, Francisco},
 title = {{An Overview of Ensemble Methods for Binary Classifiers in Multi-class Problems: Experimental Study on One-vs-one and One-vs-all Schemes}},
 journal = {Pattern Recognition},
 issue_date = {August, 2011},
 volume = {44},
 number = {8},
 month = {August},
 year = {2011},
 issn = {0031-3203},
 pages = {1761--1776},
 numpages = {16},
 publisher = {Elsevier Science Inc.},
 address = {New York, NY, USA},
 note = {Accessed August 8 2018.}
}



@book{Aho:72,
    author  = {Alfred V. Aho and Jeffrey D. Ullman},
    title   = {The Theory of Parsing, Translation and Compiling},
    year    = "1972",
    volume  = "1",
    publisher = {Prentice-Hall},
    address = {Englewood Cliffs, NJ}
}

@book{APA:83,
    author  = {{American Psychological Association}},
    title   = {Publications Manual},
    year    = "1983",
   publisher = {American Psychological Association},
   address = {Washington, DC}
}

@article{ACM:83,
	author = {Association for Computing Machinery},
	year = "1983",
	journal = {Computing Reviews},
	volume = "24",
	number = "11",
	pages = "503--512"
}

@article{Chandra:81,
	author = {Ashok K. Chandra and Dexter C. Kozen and Larry J. Stockmeyer},
	year = "1981",
	title={{Alternation}},
	journal = {Journal of the Association for Computing Machinery},
	volume = "28",
	number = "1",
	pages = "114--133",
	doi = "10.1145/322234.322243",
}

@inproceedings{andrew2007scalable,
  title={{Scalable training of {L1}-regularized log-linear models}},
  author={Andrew, Galen and Gao, Jianfeng},
  booktitle={{Proceedings of the 24th International Conference on Machine Learning}},
  pages={33--40},
  year={2007},
}

@book{Gusfield:97,
    author  = {Dan Gusfield},
    title   = {Algorithms on Strings, Trees and Sequences},
    year    = "1997",
    publisher = {Cambridge University Press},
    address = {Cambridge, UK}
}

@inproceedings{borsch2011,
	Address = {Canberra, Australia},
	Author = {Benjamin Borschinger and Mark Johnson},
	Booktitle={{Proceedings of the Australasian Language Technology Association Workshop 2011}},
	Month = {December},
	Pages = {10--18},
	title={{A Particle Filter algorithm for {B}ayesian Wordsegmentation}},
	Year = {2011}}

@article{rasooli-tetrault-2015,
    author    = {Mohammad Sadegh Rasooli and Joel R. Tetreault},
    title     = {Yara Parser: {A} Fast and Accurate Dependency Parser},
    journal   = {Computing Research Repository},
    volume    = {arXiv:1503.06733},
    year      = {2015},
    url       = {http://arxiv.org/abs/1503.06733},
    note    = {version 2}
}

@article{Ando2005,
	Acmid = {1194905},
	Author = {Ando, Rie Kubota and Zhang, Tong},
	Issn = {1532-4435},
	Issue_Date = {12/1/2005},
	Journal = {Journal of Machine Learning Research},
	Month = dec,
	Numpages = {37},
	Pages = {1817--1853},
	Publisher = {JMLR.org},
	title={{A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data}},
	Volume = {6},
	Year = {2005}}

@InProceedings{P16-1001,
  author =	"Goodman, James
  	 and Vlachos, Andreas
	     and Naradowsky, Jason",
  title =    "Noise reduction and targeted exploration in imitation learning for      Abstract Meaning Representation parsing    ",
  booktitle = 	    "Proceedings of the 54th Annual Meeting of the Association for      Computational Linguistics (Volume 1: Long Papers)    ",
  year =    "2016",
  publisher =	"Association for Computational Linguistics",
  pages =   "1--11",
  location =	"Berlin, Germany",
  doi =    "10.18653/v1/P16-1001",
  url =    "http://aclweb.org/ anthology/P16-1001"
}

@InProceedings{C14-1001,
  author =	"Harper, Mary",
  title = 	"Learning from 26 Languages: Program Management and Science in the {B}abel Program",
  booktitle = 	"Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers",
  year =    "2014",
  publisher =	"Dublin City University and Association for Computational Linguistics",
  pages =   "1",
  location =	"Dublin, Ireland",
  url =    "http://aclweb.org/ anthology/C14-1001"
}

@InProceedings{conf/sac/AraujoRPB16,
  Title                    = {An evaluation of machine translation for multilingual sentence-level sentiment analysis.},
  Author                   = {Ara{\'u}jo, Matheus and dos Reis, J{\'u}lio Cesar and Pereira, Adriano C. M. and Benevenuto, Fabr{\'i}cio},
  Booktitle                = {{SAC}},
  Year                     = {2016},
  Editor                   = {Ossowski, Sascha},
  Pages                    = {1140--1145},
  Publisher                = {ACM},

  Added-at                 = {2016-06-25T00:00:00.000+0200},
  Biburl                   = {http://www.bibsonomy.org/bibtex/2db37655323520deab602582ff47fad91/dblp},
  Crossref                 = {conf/sac/2016},
  Ee                       = {http://doi.acm.org/10.1145/2851613.2851817},
  Interhash                = {19df3151ac6300107a440ef80a34429e},
  Intrahash                = {db37655323520deab602582ff47fad91},
  ISBN                     = {978-1-4503-3739-7},
  Keywords                 = {dblp},
  Owner                    = {esohman},
  Timestamp                = {2016-06-28T12:11:17.000+0200},
  Url                      = {http://dblp.uni-trier.de /db/conf/sac/sac2016.html #AraujoRPB16},
  X-fetchedfrom            = {Bibsonomy}
}

@InProceedings{conf/wassa/Carpuat15,
  Title                    = {Connotation in Translation.},
  Author                   = {Carpuat, Marine},
  Booktitle                = {{WASSA@EMNLP}},
  Year                     = {2015},
  Editor                   = {Balahur, Alexandra and van der Goot, Erik and Vossen, Piek and Montoyo, Andr{\'e}s},
  Pages                    = {9--15},
  Publisher                = {The Association for Computer Linguistics},

  Added-at                 = {2016-09-15T00:00:00.000+0200},
  Biburl                   = {http://www.bibsonomy.org/bibtex/256d87484f8bed232c08c2ed15567b8a6/dblp},
  Crossref                 = {conf/wassa/2015},
  Ee                       = {http://aclweb.org/anthology/W/W15/W15-2903.pdf},
  Interhash                = {9d583904b0a5a82b8a0c211c0fc06bd2},
  Intrahash                = {56d87484f8bed232c08c2ed15567b8a6},
  ISBN                     = {978-1-941643-32-7},
  Keywords                 = {dblp},
  Owner                    = {esohman},
  Timestamp                = {2016-09-16T11:33:17.000+0200},
  Url                      = {http://dblp.uni-trier.de/ db/conf/wassa/wassa2015.html #Carpuat15},
  X-fetchedfrom            = {Bibsonomy}
}
@inproceedings{tackstrom2011semi,
  title={{Semi-supervised latent variable models for sentence-level sentiment analysis}},
  author={T{\"a}ckstr{\"o}m, Oscar and McDonald, Ryan},
  booktitle={{Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers-Volume 2}},
  pages={569--574},
  year={2011},
  organization={Association for Computational Linguistics}
}

@inproceedings{de2012sentisense,
  title={{SentiSense: An easily scalable concept-based affective lexicon for sentiment analysis.}},
  author={de Albornoz, Jorge Carrillo and Plaza, Laura and Gerv{\'a}s, Pablo},
  year={2012}
}
@InProceedings{conf/icde/Denecke08,
  Title                    = {Using SentiWordNet for multilingual sentiment analysis.},
  Author                   = {Denecke, Kerstin},
  Booktitle                = {{ICDE Workshops}},
  Year                     = {2008},
  Pages                    = {507--512},
  Publisher                = {IEEE Computer Society},

  Added-at                 = {2008-05-05T00:00:00.000+0200},
  Biburl                   = {http://www.bibsonomy.org/bibtex/220682fa5b5089b49fd520fd9ee941e4a/dblp},
  Crossref                 = {conf/icde/2008w},
  Ee                       = {http://dx.doi.org/10.1109/ICDEW.2008.4498370},
  Interhash                = {61555d27b90500463d27bf51924e2982},
  Intrahash                = {20682fa5b5089b49fd520fd9ee941e4a},
  ISBN                     = {978-1-4244-2161-9},
  Keywords                 = {dblp},
  Owner                    = {esohman},
  Timestamp                = {2016-04-26T11:51:37.000+0200},
  Url                      = {http://dblp.uni-trier.de/ db/conf/icde/icdew2008.html #Denecke08}
}

@InProceedings{conf/nodalida/GinterNLKHVS13,
  Title                    = {Building a Large Automatically Parsed Corpus of Finnish.},
  Author                   = {Ginter, Filip and Nyblom, Jenna and Laippala, Veronika and Kohonen, Samuel and Haverinen, Katri and Vihjanen, Simo and Salakoski, Tapio},
  Booktitle                = {{NODALIDA}},
  Year                     = {2013},
  Editor                   = {Oepen, Stephan and Hagen, Kristin and Johannessen, Janne Bondi},
  Pages                    = {291--300},
  Publisher                = {Link{\"o}ping University Electronic Press},
  Series                   = {{Link{\"o}ping Electronic Conference Proceedings}},
  Volume                   = {85},

  Added-at                 = {2014-10-05T00:00:00.000+0200},
  Biburl                   = {http://www.bibsonomy.org/bibtex/203af8f0a800ac717dadb0a124bad4a8c/dblp},
  Crossref                 = {conf/nodalida/2013},
  Ee                       = {http://www.ep.liu.se/ecp_article/index.en.aspx?issue=085 article=026},
  Interhash                = {0c32f99815cff0ea56e67e777e771b3e},
  Intrahash                = {03af8f0a800ac717dadb0a124bad4a8c},
  Keywords                 = {dblp},
  Owner                    = {esohman},
  Timestamp                = {2015-06-18T23:05:35.000+0200},
  Url                      = {http://dblp.uni-trier.de/ db/conf/nodalida/nodalida2013.html #GinterNLKHVS13},
  X-fetchedfrom            = {Bibsonomy}
}



@inproceedings{munezero2015emoTwitter,
  title={{EmoTwitter--A Fine-Grained Visualization System for Identifying Enduring Sentiments in Tweets}},
  author={Munezero, Myriam and Montero, Calkin Suero and Mozgovoy, Maxim and Sutinen, Erkki},
  booktitle={{International Conference on Intelligent Text Processing and Computational Linguistics}},
  pages={78--91},
  year={2015},
  organization={Springer}
}

@InProceedings{conf/naacl/SalamehMK15,
  Title                    = {Sentiment after Translation: A Case-Study on Arabic Social Media Posts.},
  Author                   = {Salameh, Mohammad and Mohammad, Saif and Kiritchenko, Svetlana},
  Booktitle                = {{HLT-NAACL}},
  Year                     = {2015},
  Editor                   = {Mihalcea, Rada and Chai, Joyce Yue and Sarkar, Anoop},
  Pages                    = {767--777},
  Publisher                = {The Association for Computational Linguistics},

  Added-at                 = {2015-07-11T00:00:00.000+0200},
  Biburl                   = {http://www.bibsonomy.org/bibtex/2cf6f8310631e60e1cf1e8d71cdf169d0/dblp},
  Crossref                 = {conf/naacl/2015},
  Interhash                = {d7bf5f9d71499abfc34551310e9ee832},
  Intrahash                = {cf6f8310631e60e1cf1e8d71cdf169d0},
  ISBN                     = {978-1-941643-49-5},
  Keywords                 = {dblp},
  Owner                    = {esohman},
  Timestamp                = {2015-07-14T11:40:46.000+0200},
  Url                      = {http://dblp.uni-trier.de /db/conf/naacl/naacl2015.html #SalamehMK15},
  X-fetchedfrom            = {Bibsonomy}
}





@article{bohnet2013joint,
  title={{Joint morphological and syntactic analysis for richly inflected languages}},
  author={Bohnet, Bernd and Nivre, Joakim and Boguslavsky, Igor and Farkas, Rich{\'a}rd and Ginter, Filip and Haji{\v{c}}, Jan},
  journal={{Transactions of the Association for Computational Linguistics}},
  volume={1},
  pages={415--428},
  year={2013}
}


@Book{CastellaniHafferty09,
  Title                    = {Sociology and Complexity Science: A New Field of Inquiry},
  Author                   = {Castellani, Brian and Hafferty, Frederic W.},
  Publisher                = {Springer},
  Year                     = {2009},

  Owner                    = {esohman},
  Timestamp                = {2017.10.15}
}

@InProceedings{Eisenstein11,
  Title                    = {Discovering Sociolinguistic Associations with Structured Sparsity},
  Author                   = {Eisenstein, Jacob and Smith, Noah A. and Xing, Eric P.},
  Booktitle                = {{ACL}},
  Year                     = {2011},
  Editor                   = {Lin, Dekang and Matsumoto, Yuji and Mihalcea, Rada},
  Pages                    = {1365--1374},
  Publisher                = {Association for Computer Linguistics},

  Owner                    = {esohman},
  Timestamp                = {2017.10.15}
}



@InProceedings{Hansen11,
  Title                    = {Good Friends, Bad News - Affect and Virality in Twitter},
  Author                   = {Hansen, Lars Kai and Arvidsson, Adam and Nielsen, Finn {\AA}rup and Colleoni, Elanor and Etter, Michael},
  Booktitle                = {{The 2011 International Workshop on Social Computing, Network, and Services (SocialComNet 2011)}},
  Year                     = {2011},
  Pages                    = {34--43},

  Owner                    = {esohman},
  Timestamp                = {2017.10.15}
}

@Article{journals/lre/HaverinenNVLKMOSG14,
  Title                    = {Building the essential resources for Finnish: The Turku Dependency Treebank.},
  Author                   = {Haverinen, Katri and Nyblom, Jenna and Viljanen, Timo and Laippala, Veronika and Kohonen, Samuel and Missil{\"a}, Anna and Ojala, Stina and Salakoski, Tapio and Ginter, Filip},
  Journal                  = {Language Resources and Evaluation},
  Year                     = {2014},
  Number                   = {3},
  Pages                    = {493--531},
  Volume                   = {48},

  Added-at                 = {2014-09-03T00:00:00.000+0200},
  Biburl                   = {http://www.bibsonomy.org/bibtex/254191f032029d0e2e8f06bdbefba64a9/dblp},
  Ee                       = {http://dx.doi.org/10.1007/s10579-013-9244-1},
  Interhash                = {77eaf1bf1e2710eb71d70d45bec61c69},
  Intrahash                = {54191f032029d0e2e8f06bdbefba64a9},
  Keywords                 = {dblp},
  Owner                    = {esohman},
  Timestamp                = {2015-06-18T08:43:07.000+0200},
  Url                      = {http://dblp.uni-trier.de/ db/journals/lre/lre48.html #HaverinenNVLKMOSG14},
  X-fetchedfrom            = {Bibsonomy}
}



@InProceedings{Honkela_et_al_2014,
  Title                    = {Five-Dimensional Sentiment Analysis of Corpora, Documents and Words},
  Author                   = {Honkela, Timo and Korhonen, Jaakko and Lagus, Krista and Saarinen, Esa},
  Booktitle                = {{Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 10th International Workshop, {WSOM} 2014}},
  Year                     = {2014},
  Pages                    = {209--218},

  Owner                    = {esohman},
  Timestamp                = {2017.10.15}
}



@InProceedings{shin2012annotation,
  Title                    = {Annotation Scheme for Constructing Sentiment Corpus in Korean.},
  Author                   = {Shin, Hyopil and Kim, Munhyong and Jang, Hayeon and Cattle, Andrew},
  Booktitle                = {PACLIC},
  Year                     = {2012},
  Pages                    = {181--190},

  Date-added               = {2017-10-13 11:08:02 +0000},
  Date-modified            = {2017-10-13 11:08:02 +0000}
}

@Article{shukla2011sentiment,
  Title                    = {Sentiment analysis of document based on annotation},
  Author                   = {Shukla, Archana},
  Journal                  = {arXiv preprint arXiv:1111.1648},
  Year                     = {2011},

  Date-added               = {2017-10-13 11:02:12 +0000},
  Date-modified            = {2017-10-13 11:02:12 +0000}
}

@InProceedings{steinberger2011multilingual,
  Title                    = {Multilingual Entity-Centered Sentiment Analysis Evaluated by Parallel Corpora.},
  Author                   = {Steinberger, Josef and Lenkova, Polina and Kabadjov, Mijail A and Steinberger, Ralf and Van der Goot, Erik},
  Booktitle                = {RANLP},
  Year                     = {2011},
  Pages                    = {770--775},

  Date-added               = {2017-10-13 13:00:04 +0000},
  Date-modified            = {2017-10-13 13:00:04 +0000}
}


@Article{Tan20082622,
  Title                    = {An empirical study of sentiment analysis for chinese documents},
  Author                   = {Tan, Songbo and Zhang, Jin},
  Journal                  = {Expert Systems with Applications},
  Year                     = {2008},
  Number                   = {4},
  Pages                    = {2622--2629},
  Volume                   = {34},

  Abstract                 = {Up to now, there are very few researches conducted on sentiment classification for Chinese documents. In order to remedy this deficiency, this paper presents an empirical study of sentiment categorization on Chinese documents. Four feature selection methods (MI, IG, \{CHI\} and DF) and five learning methods (centroid classifier, K-nearest neighbor, winnow classifier, Naïve Bayes and SVM) are investigated on a Chinese sentiment corpus with a size of 1021 documents. The experimental results indicate that \{IG\} performs the best for sentimental terms selection and \{SVM\} exhibits the best performance for sentiment classification. Furthermore, we found that sentiment classifiers are severely dependent on domains or topics.},
  ISSN                     = {0957-4174},
  Keywords                 = {Sentiment analysis; Information retrieval; Machine learning},
  Owner                    = {esohman},
  Timestamp                = {2017.10.15}
}


@article{von2006games,
  title={{Games with a purpose}},
  author={Von Ahn, Luis},
  journal={{Computer}},
  volume={39},
  number={6},
  pages={92--94},
  year={2006},
  publisher={IEEE}
}
@Article{journals/corr/TrompP14,
  Title                    = {Rule-based Emotion Detection on Social Media: Putting Tweets on Plutchik's Wheel.},
  Author                   = {Tromp, Erik and Pechenizkiy, Mykola},
  Journal                  = {CoRR},
  Year                     = {2014},
  Volume                   = {abs/1412.4682},

  Added-at                 = {2015-01-01T00:00:00.000+0100},
  Biburl                   = {http://www.bibsonomy.org/bibtex/2c6fce032e1edc645d6c2c05b7a57df40/dblp},
  Ee                       = {http://arxiv.org/abs/1412.4682},
  Interhash                = {1d76238994c723e36a83d3883ab58a62},
  Intrahash                = {c6fce032e1edc645d6c2c05b7a57df40},
  Keywords                 = {dblp},
  Owner                    = {esohman},
  Timestamp                = {2015-06-18T04:12:49.000+0200},
  Url                      = {http://dblp.uni-trier.de/ db/journals/corr/corr1412.htm l#TrompP14},
  X-fetchedfrom            = {Bibsonomy}
}



@InProceedings{Wan:2008:UBK:1613715.1613783,
  Title                    = {Using Bilingual Knowledge and Ensemble Techniques for Unsupervised Chinese Sentiment Analysis},
  Author                   = {Wan, Xiaojun},
  Booktitle                = {Proceedings of the Conference on Empirical Methods in Natural Language Processing},
  Year                     = {2008},

  Address                  = {Stroudsburg, PA, USA},
  Pages                    = {553--561},
  Publisher                = {Association for Computational Linguistics},
  Series                   = {{EMNLP '08}},

  Acmid                    = {1613783},
  Location                 = {Honolulu, Hawaii},
  Numpages                 = {9},
  Owner                    = {esohman},
  Timestamp                = {2017.10.15},
  Url                      = {http://dl.acm.org/citation.cfm?id=1613715.1613783}
}




@article{kakkonen2011sentiprofiler,
  title={{SentiProfiler: Creating comparable visual profiles of sentimental content in texts}},
  author={Kakkonen, Tuomo and Kakkonen, Gordana Gali{\'c}},
  journal={{Language Technologies for Digital Humanities and Cultural Heritage}},
  volume={62},
  pages={189--204},
  year={2011}
}


@inproceedings{hu2013unsupervised,
  title={{Unsupervised sentiment analysis with emotional signals}},
  author={Hu, Xia and Tang, Jiliang and Gao, Huiji and Liu, Huan},
  booktitle={{Proceedings of the 22nd international conference on World Wide Web}},
  pages={607--618},
  year={2013},
  organization={ACM}
}
@incollection{li2017reflections,
  title={{Reflections on sentiment/opinion analysis}},
  author={Li, Jiwei and Hovy, Eduard},
  booktitle={{A practical guide to sentiment analysis}},
  pages={41--59},
  year={2017},
  publisher={Springer}
}
@article{ehrmann2011building,
  title={{Building a Multilingual Named Entity-Annotated Corpus Using Annotation Projection}},
  author={Ehrmann, Maud and Turchi, Marco and Steinberger, Ralf},
  journal={{RECENT ADVANCES IN}},
  pages={118},
  year={2011}
}
@article{DBLP:journals/corr/abs-1708-03696,
  author    = {Saif M. Mohammad and
               Felipe Bravo{-}Marquez},
  title     = {Emotion intensities in tweets},
  journal   = {CoRR},
  volume    = {abs/1708.03696},
  year      = {2017},
  url       = {http://arxiv.org/abs/1708.03696},
  timestamp = {Tue, 05 Sep 2017 10:03:46 +0200},
  biburl    = {http://dblp.org/rec /bib/journals/corr/ abs-1708-03696},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}


@article{laaksonen2017working,
  title={{Working the fields of big data: Using big-data-augmented online ethnography to study candidate--candidate interaction at election time}},
  author={Laaksonen, Salla-Maaria and Nelimarkka, Matti and Tuokko, Mari and Marttila, Mari and Kekkonen, Arto and Villi, Mikko},
  journal={{Journal of Information Technology \& Politics}},
  volume={14},
  number={2},
  pages={110--131},
  year={2017},
  publisher={Taylor \& Francis}
}
@inproceedings{rees2018,
  title={{A Computational Assessment of Norwegian Literary “National Romanticism”}},
  author={Rees, Ellen},
  booktitle={{Digital Humanities in the Nordic Countries 2018}},
  year={2018},
  publisher={CEUR Workshop Proceedings}
}


@article{calefato2017emotxt,
  title={{EmoTxt: A toolkit for emotion recognition from text}},
  author={Calefato, Fabio and Lanubile, Filippo and Novielli, Nicole},
  journal={{arXiv preprint arXiv:1708.03892}},
  year={2017}
}

@article{fan2008liblinear,
  title={{LIBLINEAR: A library for large linear classification}},
  author={Fan, Rong-En and Chang, Kai-Wei and Hsieh, Cho-Jui and Wang, Xiang-Rui and Lin, Chih-Jen},
  journal={{Journal of machine learning research}},
  volume={9},
  number={Aug},
  pages={1871--1874},
  year={2008}
}

@article{hall2009weka,
  title={{The WEKA data mining software: An update}},
  author={Hall, Mark and Frank, Eibe and Holmes, Geoffrey and Pfahringer, Bernhard and Reutemann, Peter and Witten, Ian H},
  journal={{ACM SIGKDD explorations newsletter}},
  volume={11},
  number={1},
  pages={10--18},
  year={2009},
  publisher={ACM}
}


@article{abadi2015tensorflow,
  title={{TensorFlow: Large-scale machine learning on heterogeneous systems}},
  author={Abadi, Martn and Agarwal, Ashish and Barham, Paul and Brevdo, Eugene and Chen, Zhifeng and Citro, Craig and Corrado, Greg S and Davis, Andy and Dean, Jeffrey and Devin, Matthieu and others},
  journal={{URL https://www. tensorflow. org}},
  year={2015}
}

@article{sts2013evaluation,
  title={{Evaluation datasets for Twitter sentiment analysis: A survey and a new dataset, the STS-Gold}},
  author={Saif, Hassan and Fernandez, Miriam and He, Yulan and Alani, Harith},
  year={2013}
}

@inproceedings{snow2008cheap,
  title={{Cheap and fast---but is it good?: Evaluating non-expert annotations for natural language tasks}},
  author={Snow, Rion and O'Connor, Brendan and Jurafsky, Daniel and Ng, Andrew Y},
  booktitle={{Proceedings of the conference on empirical methods in natural language processing}},
  pages={254--263},
  year={2008},
  organization={Association for Computational Linguistics}
}

@inproceedings{purver2012experimenting,
  title={{Experimenting with distant supervision for emotion classification}},
  author={Purver, Matthew and Battersby, Stuart},
  booktitle={{Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics}},
  pages={482--491},
  year={2012},
  organization={Association for Computational Linguistics}
}

@inproceedings{vilares2015sentiment,
  title={{Sentiment analysis on monolingual, multilingual and code-switching Twitter corpora}},
  author={Vilares, David and Alonso, Miguel A and G{\'o}mez-Rodr{\'\i}guez, Carlos},
  booktitle={{Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis}},
  pages={2--8},
  year={2015}
}

@article{balahur2015sentiment,
  title={{Sentiment analysis system adaptation for multilingual processing: The case of tweets}},
  author={Balahur, Alexandra and Perea-Ortega, Jos{\'e} M},
  journal={{Information Processing \& Management}},
  volume={51},
  number={4},
  pages={547--556},
  year={2015},
  publisher={Elsevier}
}

@inproceedings{Devlin_2019,
    title = "{BERT}: Pre-training of Deep Bidirectional Transformers for Language Understanding",
    author = "Devlin, Jacob  and
      Chang, Ming-Wei  and
      Lee, Kenton  and
      Toutanova, Kristina",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1423",
    doi = "10.18653/v1/N19-1423",
    pages = "4171--4186",
}
@article{kiritchenko2018examining,
  title={{Examining gender and race bias in two hundred sentiment analysis systems}},
  author={Kiritchenko, Svetlana and Mohammad, Saif M},
  journal={arXiv preprint arXiv:1805.04508},
  year={2018}
}


@inproceedings{danisman2008feeler,
  title={{Feeler: Emotion classification of text using vector space model}},
  author={Danisman, Taner and Alpkocak, Adil},
  booktitle={{AISB 2008 Convention Communication, Interaction and Social Intelligence}},
  volume={1},
  pages={53},
  year={2008}
}
@inproceedings{seol2008emotion,
  title={{Emotion recognition from text using knowledge-based ANN}},
  author={Seol, Yong-Soo and Kim, Dong-Joo and Kim, Han-Woo},
  booktitle={{ITC-CSCC: International Technical Conference on Circuits Systems, Computers and Communications}},
  pages={1569--1572},
  year={2008}
}
@article{chuang2004multi,
  title={{Multi-modal emotion recognition from speech and text}},
  author={Chuang, Ze-Jing and Wu, Chung-Hsien},
  journal={{International Journal of Computational Linguistics \& Chinese Language Processing, Volume 9, Number 2, August 2004: Special Issue on New Trends of Speech and Language Processing}},
  volume={9},
  number={2},
  pages={45--62},
  year={2004}
}


@article{scikit-learn,
 title={{Scikit-learn: Machine learning in {P}ython}},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={{Journal of Machine Learning Research}},
 volume={12},
 pages={2825--2830},
 year={2011}
}



@article{ROBSON2015411,
title = "Is it all a game? Understanding the principles of gamification",
journal = "Business Horizons",
volume = "58",
number = "4",
pages = "411 - 420",
year = "2015",
issn = "0007-6813",
url = "http://www.sciencedirect.com/ science/article/pii/S000768131500035X",
author = "Karen Robson and Kirk Plangger and Jan H. Kietzmann and Ian McCarthy and Leyland Pitt",
keywords = "Gamification, Experience, Mechanics, Dynamics, Emotions, Behavior change, Motivation, American Idol"
}


%From GoEmotions bib
@inproceedings{Ghazi2015DetectingES,
  title={{Detecting Emotion Stimuli in Emotion-Bearing Sentences}},
  author={Diman Ghazi and Diana Inkpen and Stan Szpakowicz},
  booktitle={{CICLing}},
  year={2015}
}
@inproceedings{Ekman1992AnAF,
  title={{An argument for basic emotions}},
  author={Paul Ekman},
  year={1992}
}

@inproceedings{Bostan2018AnAO,
  title={{An Analysis of Annotated Corpora for Emotion Classification in Text}},
  author={Laura Ana Maria Bostan and Roman Klinger},
  booktitle={{COLING}},
  year={2018}
}
@inproceedings{Schuff2017AnnotationMA,
  title={{Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus}},
  author={Hendrik Schuff and Jeremy Barnes and Julian Mohme and Sebastian Pad{\'o} and Roman Klinger},
  booktitle={{WASSA@EMNLP}},
  year={2017}
}




@article{carl2011taxonomy,
  title={{A taxonomy of human translation styles}},
  author={Carl, Michael and Dragsted, Barbara and Jakobsen, Arnt Lykke},
  journal={{Translation journal}},
  volume={16},
  number={2},
  pages={155--168},
  year={2011},
  publisher={Accurapid, NY}
}

@inproceedings{scherrer2020tapaco,
  title={{TaPaCo: A Corpus of Sentential Paraphrases for 73 Languages}},
  author={Scherrer, Yves},
  booktitle={{Proceedings of The 12th Language Resources and Evaluation Conference}},
  pages={6868--6873},
  year={2020}
}
@inproceedings{luoma2020broad,
  title={{A Broad-coverage Corpus for Finnish Named Entity Recognition}},
  author={Luoma, Jouni and Oinonen, Miika and Pyyk{\"o}nen, Maria and Laippala, Veronika and Pyysalo, Sampo},
  booktitle={{Proceedings of The 12th Language Resources and Evaluation Conference}},
  pages={4615--4624},
  year={2020}
}

@article{agic2016multilingual,
  title={{Multilingual projection for parsing truly low-resource languages}},
  author={Agi{\'c}, {\v{Z}}eljko and Johannsen, Anders and Plank, Barbara and Alonso, H{\'e}ctor Mart{\'\i}nez and Schluter, Natalie and S{\o}gaard, Anders},
  journal={{Transactions of the Association for Computational Linguistics}},
  volume={4},
  pages={301--312},
  year={2016},
  publisher={MIT Press}
}


}
@inproceedings{schuff2017annotation,
  title={{Annotation, modelling and analysis of fine-grained emotions on a stance and sentiment detection corpus}},
  author={Schuff, Hendrik and Barnes, Jeremy and Mohme, Julian and Pad{\'o}, Sebastian and Klinger, Roman},
  booktitle={{Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis}},
  pages={13--23},
  year={2017}
}

@inproceedings{Hsu2018SocialNLP2E,
  title={{SocialNLP 2018 EmotionX Challenge Overview: Recognizing Emotions in Dialogues}},
  author={Chao-Chun Hsu and Lun-Wei Ku},
  booktitle={{SocialNLP@ACL}},
  year={2018}
}



@article{DBLP:journals/corr/MohammadY13,
  author    = {Saif M. Mohammad and
               Tony Yang},
  title     = {Tracking Sentiment in Mail: How Genders Differ on Emotional Axes},
  journal   = {CoRR},
  volume    = {abs/1309.6347},
  year      = {2013},
  url       = {http://arxiv.org/abs/1309.6347},
  archivePrefix = {arXiv},
  eprint    = {1309.6347},
  timestamp = {Mon, 13 Aug 2018 16:46:38 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/MohammadY13.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/Mohammad13a,
  author    = {Saif Mohammad},
  title     = {From Once Upon a Time to Happily Ever After: Tracking Emotions in
               Novels and Fairy Tales},
  journal   = {CoRR},
  volume    = {abs/1309.5909},
  year      = {2013},
  url       = {http://arxiv.org/abs/1309.5909},
  archivePrefix = {arXiv},
  eprint    = {1309.5909},
  timestamp = {Mon, 13 Aug 2018 16:49:01 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/Mohammad13a.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{pirinen2015omorfi,
  title={Omorfi—free and open source morphological lexical database for Finnish},
  author={Pirinen, Tommi A},
  booktitle={Proceedings of the 20th Nordic conference of computational linguistics (NODALIDA 2015)},
  pages={313--315},
  year={2015}
}
@inproceedings{maas2011learning,
  title={Learning word vectors for sentiment analysis},
  author={Maas, Andrew and Daly, Raymond E and Pham, Peter T and Huang, Dan and Ng, Andrew Y and Potts, Christopher},
  booktitle={Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies},
  pages={142--150},
  year={2011}
}
@article{yu2017refining,
  title={Refining word embeddings using intensity scores for sentiment analysis},
  author={Yu, Liang-Chih and Wang, Jin and Lai, K Robert and Zhang, Xuejie},
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  volume={26},
  number={3},
  pages={671--681},
  year={2017},
  publisher={IEEE}
}
@inproceedings{ye2018encoding,
  title={Encoding sentiment information into word vectors for sentiment analysis},
  author={Ye, Zhe and Li, Fang and Baldwin, Timothy},
  booktitle={Proceedings of the 27th international conference on computational linguistics},
  pages={997--1007},
  year={2018}
}
@article{silfverberg2016finnpos,
  title={FinnPos: an open-source morphological tagging and lemmatization toolkit for Finnish},
  author={Silfverberg, Miikka and Ruokolainen, Teemu and Lind{\'e}n, Krister and Kurimo, Mikko},
  journal={Language Resources and Evaluation},
  volume={50},
  number={4},
  pages={863--878},
  year={2016},
  publisher={Springer}
}

@inproceedings{kettunen2022geographic,
title={{Geographic Space in Pentti Haanpaa’s Novel Korpisotaa – where does the War Happen?}},
author={Kimmo Kettunen},
booktitle={{Proceedings of the 6th Digital Humanities in the Nordic and Baltic countries conference}},
year={2022},
pages={297--307}
}
@inproceedings{mohammad2018semeval,
  title={{Semeval-2018 task 1: Affect in tweets}},
  author={Mohammad, Saif and Bravo-Marquez, Felipe and Salameh, Mohammad and Kiritchenko, Svetlana},
  booktitle={{Proceedings of the 12th international workshop on semantic evaluation}},
  pages={1--17},
  year={2018}
 }
@article{uralicnlp_2019, 
    title={{UralicNLP}: An {NLP} Library for {U}ralic Languages},
    DOI={10.21105/joss.01345}, 
    journal={Journal of Open Source Software}, 
    author={Mika Hämäläinen}, 
    year={2019}, 
    volume={4},
    number={37},
    pages={1345}
}

@article{kanerva2020lemmatizer,
title={Universal Lemmatizer: A Sequence to Sequence Model for Lemmatizing Universal Dependencies Treebanks},
author={Kanerva, Jenna and Ginter, Filip and Salakoski, Tapio},
year={2020},
journal={Natural Language Engineering},
publisher={Cambridge University Press},
DOI={10.1017/S1351324920000224},
pages={1--30},
url={http://dx.doi.org/10.1017/S1351324920000224}
}

@article{ardia2017r,
  title={{The R package sentometrics to compute, aggregate and predict with textual sentiment}},
  author={Ardia, David and Bluteau, Keven and Borms, Samuel and Boudt, Kris},
  journal={Aggregate and Predict with Textual Sentiment (November 9, 2017)},
  year={2017}
}


@inproceedings{zehe2016prediction,
  title={{Prediction of happy endings in German novels based on sentiment information}},
  author={Zehe, Albin and Becker, Martin and Hettinger, Lena and Hotho, Andreas and Reger, Isabella and Jannidis, Fotis},
  booktitle={{3rd Workshop on Interactions between Data Mining and Natural Language Processing, Riva del Garda, Italy}},
  pages={9},
  year={2016}
}

@inproceedings{zhe2002text,
  title={{Text-to-emotion engine for real time internet communication}},
  author={Zhe, Xu and Boucouvalas, AC},
  booktitle={{Proceedings of International Symposium on Communication Systems, Networks and DSPs}},
  pages={164--168},
  year={2002},
  organization={{Citeseer}}
}



@INPROCEEDINGS{jaya2018,  
author={R. Jayakrishnan and G. N. Gopal and M. S. Santhikrishna},  booktitle={{2018 International Conference on Computer Communication and Informatics (ICCCI)}},   
title={{Multi-Class Emotion Detection and Annotation in Malayalam Novels}},   year={2018}},  
pages={1-5}
}

@book{elkins_shapes_2022,
	title = {The Shapes of Stories: Sentiment Analysis for Narrative},
	isbn = {978-1-00-927040-3 978-1-00-927039-7},
	shorttitle = {The {Shapes} of {Stories}},
	url = {https://www.cambridge.org/core/product/identifier/9781009270403/type/element},
	abstract = {Sentiment analysis has gained widespread adoption in many ﬁelds but not – until now – in literary studies. Scholars have lacked a robust methodology that adapts the tool to the skills and questions central to literary scholars. Also lacking has been quantitative data to help the scholar choose between the many models. Which model is best for which narrative and why? By comparing more than three dozen models, including the latest deep learning artiﬁcial intelligence (AI), the author details how to choose the correct model – or set of models – depending on the unique aﬀective ﬁngerprint of a narrative. The author also demonstrates how to combine a clustered close reading of textual cruxes in order to interpret a narrative. By analyzing a diverse and cross-cultural range of texts in a series of case studies, the Element highlights new insights into the many shapes of stories.},
	language = {en},
	urldate = {2023-09-25},
	publisher = {Cambridge University Press},
	author = {Elkins, Katherine},
	month = jul,
	year = {2022},
	doi = {10.1017/9781009270403},
	file = {Elkins - 2022 - The Shapes of Stories Sentiment Analysis for Narr.pdf:/Users/au324704/Zotero/storage/7T8D6LDN/Elkins - 2022 - The Shapes of Stories Sentiment Analysis for Narr.pdf:application/pdf},
}

@misc{chun_sentimentarcs_2021,
	title = {{SentimentArcs}: A Novel Method for Self-Supervised Sentiment Analysis of Time Series Shows {SOTA} {Transformers} Can Struggle Finding Narrative Arcs},
	shorttitle = {{SentimentArcs}},
	url = {http://arxiv.org/abs/2110.09454},
	abstract = {SOTA Transformer and DNN short text sentiment classifiers report over 97\% accuracy on narrow domains like IMDB movie reviews. Real-world performance is significantly lower because traditional models overfit benchmarks and generalize poorly to different or more open domain texts. This paper introduces SentimentArcs, a new self-supervised time series sentiment analysis methodology that addresses the two main limitations of traditional supervised sentiment analysis: limited labeled training datasets and poor generalization. A large ensemble of diverse models provides a synthetic ground truth for self-supervised learning. Novel metrics jointly optimize an exhaustive search across every possible corpus:model combination. The joint optimization over both the corpus and model solves the generalization problem. Simple visualizations exploit the temporal structure in narratives so domain experts can quickly spot trends, identify key features, and note anomalies over hundreds of arcs and millions of data points. To our knowledge, this is the first self-supervised method for time series sentiment analysis and the largest survey directly comparing real-world model performance on long-form narratives.},
	urldate = {2023-09-29},
	publisher = {arXiv},
	author = {Chun, Jon},
	month = oct,
	year = {2021},
	note = {arXiv:2110.09454 [cs]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning},
	annote = {Comment: 87 pages, 97 figures},
	file = {arXiv.org Snapshot:/Users/au324704/Zotero/storage/RGBAYKAE/2110.html:text/html;Full Text PDF:/Users/au324704/Zotero/storage/BAICKCRN/Chun - 2021 - SentimentArcs A Novel Method for Self-Supervised .pdf:application/pdf},
}

@inproceedings{francisco2006automated,
  title={{Automated mark up of affective information in english texts}},
  author={Francisco, Virginia and Gerv{\'a}s, Pablo},
  booktitle={{International Conference on Text, Speech and Dialogue}},
  pages={375--382},
  year={2006},
  organization={{Springer}}
}

@inproceedings{nalisnick2013extracting,
  title={{Extracting sentiment networks from Shakespeare's plays}},
  author={Nalisnick, Eric T and Baird, Henry S},
  booktitle={{2013 12th International Conference on Document Analysis and Recognition}},
  pages={758--762},
  year={2013},
  organization={{IEEE}}
}

@inproceedings{lin2019secret,
  title={{The Secret to Popular Chinese Web Novels: A Corpus-Driven Study}},
  author={Lin, Yi-Ju and Hsieh, Shu-Kai},
  booktitle={{2nd Conference on Language, Data and Knowledge (LDK 2019)}},
  year={2019},
  organization={{Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik}}
}


@article{kurikka2013algot,
  title={{Algot Untola ja kirjoittava kone}},
  author={Kurikka, Kaisa},
  year={2013},
  publisher={{Turku: Eetos}}
}


@article{maijala2008passion,
  title={{Passion vallassa: Hermostunut aika Minna Canthin teoksissa}},
  author={Maijala, Minna},
  journal={SKST 1169},
  year={2008},
  publisher={{Suomalaisen Kirjallisuuden Seura}}
}





@article{wilkens2015digital,
  title={{Digital humanities and its application in the study of literature and culture}},
  author={Wilkens, Matthew},
  journal={Comparative Literature},
  volume={67},
  number={1},
  pages={11--20},
  year={2015},
  publisher={{University of Oregon}}
}
@article{schmidt2016digital,
  title={{Do digital humanists need to understand algorithms?}},
  author={Schmidt, Benjamin M},
  journal={Debates in the Digital Humanities},
  volume={2016},
  pages={53},
  year={2016},
  publisher={{JSTOR}}
}
@misc{lebert2008project,
  title={{Project Gutenberg (1971-2008)}},
  author={Lebert, Marie},
  year={2008},
  publisher={{Project Gutenberg}}
}

@article{piper2015novel,
  title={{Novel devotions: Conversional reading, computational modeling, and the modern novel}},
  author={Piper, Andrew},
  journal={New Literary History},
  volume={46},
  number={1},
  pages={63--98},
  year={2015},
  publisher={{Johns Hopkins University Press}}
}

@article{aslam2020sentiments,
  title={{Sentiments and emotions evoked by news headlines of coronavirus disease (COVID-19) outbreak}},
  author={Aslam, Faheem and Awan, Tahir Mumtaz and Syed, Jabir Hussain and Kashif, Aisha and Parveen, Mahwish},
  journal={Humanities and Social Sciences Communications},
  volume={7},
  number={1},
  pages={1--9},
  year={2020},
  publisher={{Palgrave}}
}
@inproceedings{werder2018evolution,
  title={{The evolution of emotional displays in open source software development teams: An individual growth curve analysis}},
  author={Werder, Karl},
  booktitle={{2018 IEEE/ACM 3rd International Workshop on Emotion Awareness in Software Engineering (SEmotion)}},
  pages={1--6},
  year={2018},
  organization={{IEEE}}
}


@InCollection{kajava2020emotion,
  author        = {Kajava, Kaisla SA and {\"O}hman, Emily Sofi and Hui, Piao and Tiedemann, Jörg},
  title         = {{Emotion Preservation in Translation: Evaluating Datasets for Annotation Projection}},
  booktitle     = {{Digital Humanities in the Nordic Countries 2020}},
  publisher     = {CEUR Workshop Proceedings},
  year          = {2020},
  __markedentry = {[esohman:5]},
}

@InCollection{ohman2020challenges,
  author        = {{\"O}hman, Emily},
  title         = {{Challenges in Annotation: Annotator Experiences from a Crowdsourced Emotion Annotation Task}},
  booktitle     = {{Digital Humanities in the Nordic Countries 2020}},
  publisher     = {CEUR Workshop Proceedings},
  year          = {2020},
  __markedentry = {[esohman:5]},
}

@Article{ohman2016challenges,
  author        = {{\"O}hman, Emily and Honkela, Timo and Tiedemann, Jörg},
  title         = {{The Challenges of Multi-dimensional Sentiment Analysis Across Languages}},
  journal       = {{PEOPLES 2016}},
  year          = {2016},
  pages         = {138},
  __markedentry = {[esohman:5]},
  date-added    = {2017-10-15 08:45:40 +0000},
  date-modified = {2017-10-15 08:45:40 +0000},
    organization  = {Association for Computational Linguistics}
}

@InProceedings{ohman-senti2018,
  author        = {{\"O}hman, Emily and Kajava, Kaisla},
  title         = {{Sentimentator: Gamifying Fine-grained Sentiment Annotation}},
  booktitle     = {Digital Humanities in the Nordic Countries 2018},
  year          = {2018},
  publisher     = {CEUR Workshop Proceedings},
  __markedentry = {[esohman:5]},
}

@InProceedings{ohman2018creating,
  author        = {{\"O}hman, Emily Sofi and Tiedemann, Jörg and Honkela, Timo and Kajava, Kaisla},
  title         = {Creating a Dataset for Multilingual Fine-grained Emotion-detection Using Gamification-based Annotation},
  booktitle     = {Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis},
  year          = {2018},
  organization  = {Association for Computational Linguistics},
  __markedentry = {[esohman:5]},
}

@InProceedings{ohman2019teaching,
  author        = {Emily {\"O}hman},
  title         = {{Teaching Computational Methods to Humanities Students}},
  booktitle     = {{Digital Humanities in the Nordic Countries 2019}},
  year          = {2019},
  publisher     = {CEUR Workshop Proceedings},
  __markedentry = {[esohman:5]},
}

@InProceedings{LT@offenseval,
  author    = {Marc Pàmies and {\"O}hman, Emily and Kajava, Kaisla and Tiedemann, Jörg},
  title     = {{LT@Helsinki at SemEval-2020 Task 12: Multilingual or language-specific BERT?}},
  booktitle = {Proceedings of the 14th International Workshop on Semantic Evaluation},
  year      = {2020},
}

%Not yet published articles (placeholders)

%COLING paper on XED
@InProceedings{ohman2020xed,
    title = "{XED}: A Multilingual Dataset for Sentiment Analysis and Emotion Detection",
    author = {{\"O}hman, Emily  and
      P{\`a}mies, Marc  and
      Kajava, Kaisla  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://aclanthology.org/2020.coling-main.575",
    pages = "6542--6552",
}

%FPPP paper
@article{ohman2020FPPP,
author = {Juha Koljonen and {\"O}hman, Emily and Ahonen, Pertti and Mattila, Mikko},
title = {{Strategic sentiments and emotions in post-Second World War party manifestos in Finland}},
year = {2022-forthcoming},
journal = {Journal of Computational Social Science}
}
@inproceedings{partanen2019dialect,
  title={Dialect text normalization to normative standard finnish},
  author={Partanen, Niko and H{\"a}m{\"a}l{\"a}inen, Mika and Alnajjar, Khalid},
  booktitle={The Fifth Workshop on Noisy User-generated Text (W-NUT 2019)},
  year={2019},
  organization={The Association for Computational Linguistics}
}
%FPPP paper
@incollection{herrmann2019linguistic,
  title={Linguistic metaphor identification in German},
  author={Herrmann, J Berenike and Woll, Karola and Dorst, Aletta G},
  booktitle={Metaphor Identification in Multiple Languages. MIPVU around the world},
   pages={114--135},
  year={2019},
  publisher={John Benjamins}
}

@book{kim2007affective,
  title={The affective turn: Theorizing the social},
  author={Kim, Hosu and Bianco, Jamie},
  year={2007},
  publisher={Duke University Press}
}


@article{perry1979literary,
  title={Literary dynamics: how the order of a text creates its meanings [with an analysis of Faulkner's" A Rose for Emily"]},
  author={Perry, Menakhem},
  journal={Poetics today},
  volume={1},
  number={1/2},
  pages={35--361},
  year={1979},
  publisher={JSTOR}
}
@book{airio2009morphological,
  title={Morphological Problems in IR and CLIR. Applying linguistic methods and approximate string matching tools},
  author={Airio, Eija},
  year={2009},
  publisher={Tampere University Press}
}
@inproceedings{ohman2022self,
  title={{SELF \& FEIL: Emotion Lexicons for Finnish}},
  author={{\"O}hman, Emily},
  booktitle={Proceedings of the 6th Digital Humanities in the Nordic and Baltic Countries conference},
  year={2022},
  publisher     = {CEUR Workshop Proceedings}
}

@article{hamalainen2021lemmatization,
  title={Lemmatization of historical old literary Finnish texts in modern orthography},
  author={H{\"a}m{\"a}l{\"a}inen, Mika and Partanen, Niko and Alnajjar, Khalid},
  journal={arXiv preprint arXiv:2107.03266},
  year={2021}
}
@book{allwood2007european,
  title={European intercultural workplace: Sverige},
  author={Allwood, Jens and Lindstr{\"o}m, Natalia Berbyuk and B{\"o}rjesson, Margreth and Edeb{\"a}ck, Charlotte and Myhre, Randi and Voionmaa, Kaarlo and {\"O}hman, Emily},
  year={2007},
  publisher={Gothenburg University}
}
@article{nevalainen2016language,
  title={Language Change Database: A new online resource},
  author={Nevalainen, Terttu and Vartiainen, Turo and S{\"a}ily, Tanja and Kes{\"a}niemi, Joonas and Dominowska, Agata and {\"O}hman, Emily},
  journal={ICAME journal},
  volume={40},
  number={1},
  pages={77--94},
  year={2016},
  publisher={Sciendo}
}
@article{van2021validity,
  title={The Validity of Sentiment Analysis: Comparing Manual Annotation, Crowd-Coding, Dictionary Approaches, and Machine Learning Algorithms},
  author={van Atteveldt, Wouter and van der Velden, Mariken ACG and Boukes, Mark},
  journal={Communication Methods and Measures},
  volume={15},
  number={2},
  pages={121--140},
  year={2021},
  publisher={Taylor \& Francis}
}

@book{nykros2020,
    author={Nykänen, Elise and Rossi, Riikka},
    title={{Suomalaiset ahdistukset. Kansallinen omakuva ja kielteiset tunteet}},
    publisher={{Kotimaisen kirjallisuuden vuosikirja Joutsen/Svanen -erikoisnumero}},
    year={2020}
}
@article{roberts2019stm,
  title={Stm: An R package for structural topic models},
  author={Roberts, Margaret E and Stewart, Brandon M and Tingley, Dustin},
  journal={Journal of Statistical Software},
  volume={91},
  pages={1--40},
  year={2019}
}
@inproceedings{gniady2017open,
  title={{An Open, Reproducible Method for Teaching Text Analysis with R.}},
  author={Gniady, Tassie and Wernert, Eric A},
  booktitle={{DH}},
  year={2017}
}
@article{bravo2014meta,
  title={{Meta-level sentiment models for big social data analysis}},
  author={Bravo-Marquez, Felipe and Mendoza, Marcelo and Poblete, Barbara},
  journal={Knowledge-based systems},
  volume={69},
  pages={86--99},
  year={2014},
  publisher={Elsevier}
}
@incollection{mohammad2016sentiment,
  title={{Sentiment analysis: Detecting valence, emotions, and other affectual states from text}},
  author={Mohammad, Saif M},
  booktitle={{Emotion measurement}},
  pages={201--237},
  year={2016},
  publisher={Elsevier}
}
@article{vosoughi2018spread,
  title={{The spread of true and false news online}},
  author={Vosoughi, Soroush and Roy, Deb and Aral, Sinan},
  journal={Science},
  volume={359},
  number={6380},
  pages={1146--1151},
  year={2018},
  publisher={American Association for the Advancement of Science}
}
@InProceedings{C14-1001,
  author =	"Harper, Mary",
  title = 	"Learning from 26 Languages: Program Management and Science in the Babel Program",
  booktitle = 	"Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers",
  year =    "2014",
  publisher =	"Dublin City University and Association for Computational Linguistics",
  pages =   "1",
  location =	"Dublin, Ireland",
  url =    "http://aclweb.org/anthology/C14-1001"
}

@inproceedings{wordnet,
  title={Wordnet affect: an affective extension of wordnet.},
  author={Strapparava, Carlo and Valitutti, Alessandro and others},
  booktitle={Proceedings of the {International Conference on Language Resources and Evaluation (LREC)}},
  volume={4},
  number={1083-1086},
  pages={40},
  year={2004}
}


@article{paakkonen2022uncoco,
author = {Paakkonen, J. and {\"O}hman, E. and Laaksonen, S-M.},
year = {Forthcoming} ,
title = {Unconventional Communicators in the COVID-19 Crisis.}
}
@book{Aho:72,
    author  = {Alfred V. Aho and Jeffrey D. Ullman},
    title   = {The Theory of Parsing, Translation and Compiling},
    year    = "1972",
    volume  = "1",
    publisher = {Prentice-Hall},
    address = {Englewood Cliffs, NJ}
}

@book{APA:83,
    author  = {{American Psychological Association}},
    title   = {Publications Manual},
    year    = "1983",
    publisher = {American Psychological Association},
    address = {Washington, DC}
}

@article{Chandra:81,
	author = {Ashok K. Chandra and Dexter C. Kozen and Larry J. Stockmeyer},
	year = "1981",
	title = {Alternation},
	journal = {Journal of the Association for Computing Machinery},
	volume = "28",
	number = "1",
	pages = "114--133",
	doi = "10.1145/322234.322243",
}

@inproceedings{andrew2007scalable,
  title={Scalable training of {L1}-regularized log-linear models},
  author={Andrew, Galen and Gao, Jianfeng},
  booktitle={Proceedings of the 24th International Conference on Machine Learning},
  pages={33--40},
  year={2007},
}

@article{gebru2021datasheets,
  title={Datasheets for datasets},
  author={Gebru, Timnit and Morgenstern, Jamie and Vecchione, Briana and Vaughan, Jennifer Wortman and Wallach, Hanna and Iii, Hal Daum{\'e} and Crawford, Kate},
  journal={Communications of the ACM},
  volume={64},
  number={12},
  pages={86--92},
  year={2021},
  publisher={ACM New York, NY, USA}
}
@article{mohammad2022ethics,
  title={Ethics sheet for automatic emotion recognition and sentiment analysis},
  author={Mohammad, Saif M},
  journal={Computational Linguistics},
  volume={48},
  number={2},
  pages={239--278},
  year={2022},
  publisher={MIT Press One Broadway, 12th Floor, Cambridge, Massachusetts 02142, USA~…}
}

@inproceedings{hiippala2022developing,
  title={Developing a tool for fair and reproducible use of paid crowdsourcing in the digital humanities},
  author={Hiippala, Tuomo and Hotti, Helmiina and Suviranta, Rosa},
  booktitle={Proceedings of the 6th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature},
  pages={7--12},
  year={2022}
}

@inproceedings{zhou-etal-2022-problems,
    title = "Problems with Cosine as a Measure of Embedding Similarity for High Frequency Words",
    author = "Zhou, Kaitlyn  and
      Ethayarajh, Kawin  and
      Card, Dallas  and
      Jurafsky, Dan",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-short.45",
    doi = "10.18653/v1/2022.acl-short.45",
    pages = "401--423",
    abstract = "Cosine similarity of contextual embeddings is used in many NLP tasks (e.g., QA, IR, MT) and metrics (e.g., BERTScore). Here, we uncover systematic ways in which word similarities estimated by cosine over BERT embeddings are understated and trace this effect to training data frequency. We find that relative to human judgements, cosine similarity underestimates the similarity of frequent words with other instances of the same word or other words across contexts, even after controlling for polysemy and other factors. We conjecture that this underestimation of similarity for high frequency words is due to differences in the representational geometry of high and low frequency words and provide a formal argument for the two-dimensional case.",
}

@book{Gusfield:97,
    author  = {Dan Gusfield},
    title   = {Algorithms on Strings, Trees and Sequences},
    year    = "1997",
    publisher = {Cambridge University Press},
    address = {Cambridge, UK}
}

@article{bizzoni2021sentiment,
  title={Sentiment Dynamics of Success: Fractal Scaling of Story Arcs Predicts Reader Preferences},
  author={Bizzoni, Yuri and Peura, Telma and Thomsen, Mads Rosendahl and Nielbo, Kristoffer},
  journal={NLP4DH 2021},
  pages={1},
  year={2021}
}

@article{rasooli-tetrault-2015,
    author    = {Mohammad Sadegh Rasooli and Joel R. Tetreault},
    title     = {Yara Parser: {A} Fast and Accurate Dependency Parser},
    journal   = {Computing Research Repository},
    volume    = {arXiv:1503.06733},
    year      = {2015},
    url       = {http://arxiv.org/abs/1503.06733},
    note    = {version 2}
}

@article{Ando2005,
	Acmid = {1194905},
	Author = {Ando, Rie Kubota and Zhang, Tong},
	Issn = {1532-4435},
	Issue_Date = {12/1/2005},
	Journal = {Journal of Machine Learning Research},
	Month = dec,
	Numpages = {37},
	Pages = {1817--1853},
	Publisher = {JMLR.org},
	Title = {A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data},
	Volume = {6},
	Year = {2005}
}


@book{bal2009narratology,
  title={Narratology: Introduction to the theory of narrative},
  author={Bal, Mieke and Van Boheemen, Christine},
  year={2009},
  publisher={University of Toronto Press}
}

@inproceedings{somasundaran-etal-2020-emotion,
    title = "Emotion Arcs of Student Narratives",
    author = "Somasundaran, Swapna  and
      Chen, Xianyang  and
      Flor, Michael",
    booktitle = "Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.nuse-1.12",
    doi = "10.18653/v1/2020.nuse-1.12",
    pages = "97--107",
    abstract = "This paper studies emotion arcs in student narratives. We construct emotion arcs based on event affect and implied sentiments, which correspond to plot elements in the story. We show that student narratives can show elements of plot structure in their emotion arcs and that properties of these arcs can be useful indicators of narrative quality. We build a system and perform analysis to show that our arc-based features are complementary to previously studied sentiment features in this area.",
}

@article{haider2020po,
  title={PO-EMO: Conceptualization, annotation, and modeling of aesthetic emotions in German and English poetry},
  author={Haider, Thomas and Eger, Steffen and Kim, Evgeny and Klinger, Roman and Menninghaus, Winfried},
  journal={arXiv preprint arXiv:2003.07723},
  year={2020}
}


@book{kokoska2000crc,
  title={CRC standard probability and statistics tables and formulae},
  author={Kokoska, Stephen and Zwillinger, Daniel},
  year={2000},
  publisher={Crc Press}
}

@article{su2012linear,
  title={Linear regression},
  author={Su, Xiaogang and Yan, Xin and Tsai, Chih-Ling},
  journal={Wiley Interdisciplinary Reviews: Computational Statistics},
  volume={4},
  number={3},
  pages={275--294},
  year={2012},
  publisher={Wiley Online Library}
}

@article{mar2011emotion,
  title={Emotion and narrative fiction: Interactive influences before, during, and after reading},
  author={Mar, Raymond A and Oatley, Keith and Djikic, Maja and Mullin, Justin},
  journal={Cognition \& emotion},
  volume={25},
  number={5},
  pages={818--833},
  year={2011},
  publisher={Taylor \& Francis}
}

@article{denecke2015sentiment,
  title={Sentiment analysis in medical settings: New opportunities and challenges},
  author={Denecke, Kerstin and Deng, Yihan},
  journal={Artificial intelligence in medicine},
  volume={64},
  number={1},
  pages={17--27},
  year={2015},
  publisher={Elsevier}
}

@article{verdaasdonk1983social,
  title={Social and economic factors in the attribution of literary quality},
  author={Verdaasdonk, Hugo},
  journal={Poetics},
  volume={12},
  number={4-5},
  pages={383--395},
  year={1983},
  publisher={Elsevier}
}



@inproceedings{fritsch_automatic_2019,
	address = {Brighton, United Kingdom},
	title = {Automatic {Diagnosis} of {Alzheimer}’s {Disease} {Using} {Neural} {Network} {Language} {Models}},
	isbn = {978-1-4799-8131-1},
	url = {https://ieeexplore.ieee.org/document/8682690/},
	doi = {10.1109/ICASSP.2019.8682690},
	urldate = {2023-12-12},
	booktitle = {{ICASSP} 2019 - 2019 {IEEE} {International} {Conference} on {Acoustics}, {Speech} and {Signal} {Processing} ({ICASSP})},
	author = {Fritsch, Julian and Wankerl, Sebastian and Noth, Elmar},
	month = may,
	year = {2019},
	pages = {5841--5845},
	file = {paper_57.pdf:/Users/au324704/Zotero/storage/6IGX64SF/paper_57.pdf:application/pdf;Submitted Version:/Users/au324704/Zotero/storage/37ZU4FII/Fritsch et al. - 2019 - Automatic Diagnosis of Alzheimer’s Disease Using N.pdf:application/pdf},
}


@misc{zhang_trading_2020,
	title = {Trading Off Diversity and Quality in Natural Language Generation},
	url = {http://arxiv.org/abs/2004.10450},
	abstract = {For open-ended language generation tasks such as storytelling and dialogue, choosing the right decoding algorithm is critical to controlling the tradeoff between generation quality and diversity. However, there presently exists no consensus on which decoding procedure is best or even the criteria by which to compare them. We address these issues by casting decoding as a multi-objective optimization problem aiming to simultaneously maximize both response quality and diversity. Our framework enables us to perform the first large-scale evaluation of decoding methods along the entire quality-diversity spectrum. We find that when diversity is a priority, all methods perform similarly, but when quality is viewed as more important, the recently proposed nucleus sampling (Holtzman et al. 2019) outperforms all other evaluated decoding algorithms. Our experiments also confirm the existence of the `likelihood trap', the counter-intuitive observation that high likelihood sequences are often surprisingly low quality. We leverage our findings to create and evaluate an algorithm called {\textbackslash}emph\{selective sampling\} which tractably approximates globally-normalized temperature sampling.},
	urldate = {2023-12-12},
	publisher = {arXiv},
	author = {Zhang, Hugh and Duckworth, Daniel and Ippolito, Daphne and Neelakantan, Arvind},
	month = apr,
	year = {2020},
	keywords = {Computer Science - Computation and Language},
}

@article{kovacs_paradox_2014,
	title = {The Paradox of Publicity},
	volume = {1},
	doi = {10.1177/0001839214523602},
	journal = {Administrative Science Quarterly},
	author = {Kovács, Balázs and Sharkey, Amanda J},
	year = {2014},
	pages = {1--33},
}

@article{barre_operationalizing_2023,
	title = {Operationalizing {Canonicity}: {A} {Quantitative} {Study} of {French} 19th and 20th {Century} {Literature}},
	volume = {8},
	shorttitle = {Operationalizing {Canonicity}},
	url = {https://culturalanalytics.org/article/88113-operationalizing-canonicity-a-quantitative-study-of-french-19th-and-20th-century-literature},
	doi = {10.22148/001c.88113},
	abstract = {This article delves into the literary canon, a concept shaped by social biases and influenced by successive receptions. The canonization process is a multifaceted phenomenon, emerging from the intricate interplay of sociological, economic, and political factors. Our objective is to detect the underlying textual dynamics that grant certain works exceptional longevity while jeopardizing the transmission of the majority. Drawing on various criteria, we present an operational framework for defining the French literary canon, centered on its contemporary reception and emphasizing the role of institutions, particularly schools, in its formation. Leveraging natural language processing and machine learning techniques, we unveil an intrinsic norm inherent to the literary canon. Through statistical modeling, we achieve predictive outcomes with accuracy ranging from 70\% to 74\%, contingent on the chosen scale of canonicity. We believe that these findings detect what Charles Altieri calls a “cultural grammar”, referring to the idea that canonical works in literature serve as foundational texts that shape the norms, values, and conventions of a particular cultural tradition. We posit that this linguistic norm arises from biased latent selection mechanisms linked to the role of the educational system in the canon-formation process.},
	language = {en},
	number = {3},
	urldate = {2023-12-13},
	journal = {Journal of Cultural Analytics},
	author = {Barré, Jean and Camps, Jean-Baptiste and Poibeau, Thierry},
	month = oct,
	year = {2023},
	file = {Full Text PDF:/Users/au324704/Zotero/storage/25DLDXTY/Barré et al. - 2023 - Operationalizing Canonicity A Quantitative Study .pdf:application/pdf},
}


@article{martinc_supervised_2021,
	title = {Supervised and {Unsupervised} {Neural} {Approaches} to {Text} {Readability}},
	volume = {47},
	url = {https://aclanthology.org/2021.cl-1.6},
	doi = {10.1162/coli_a_00398},
	abstract = {We present a set of novel neural supervised and unsupervised approaches for determining the readability of documents. In the unsupervised setting, we leverage neural language models, whereas in the supervised setting, three different neural classification architectures are tested. We show that the proposed neural unsupervised approach is robust, transferable across languages, and allows adaptation to a specific readability task and data set. By systematic comparison of several neural architectures on a number of benchmark and new labeled readability data sets in two languages, this study also offers a comprehensive analysis of different neural approaches to readability classification. We expose their strengths and weaknesses, compare their performance to current state-of-the-art classification approaches to readability, which in most cases still rely on extensive feature engineering, and propose possibilities for improvements.},
	number = {1},
	urldate = {2023-12-11},
	journal = {Computational Linguistics},
	author = {Martinc, Matej and Pollak, Senja and Robnik-Šikonja, Marko},
	month = mar,
	year = {2021},
	note = {Place: Cambridge, MA
Publisher: MIT Press},
	pages = {141--179},
	file = {Full Text PDF:/Users/au324704/Zotero/storage/PV2WNYJ8/Martinc et al. - 2021 - Supervised and Unsupervised Neural Approaches to T.pdf:application/pdf},
}


@article{oh-schuler-2023-surprisal,
    title = "Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times?",
    author = "Oh, Byung-Doh  and
      Schuler, William",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "11",
    year = "2023",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://aclanthology.org/2023.tacl-1.20",
    doi = "10.1162/tacl_a_00548",
    pages = "336--350",
    abstract = "This work presents a linguistic analysis into why larger Transformer-based pre-trained language models with more parameters and lower perplexity nonetheless yield surprisal estimates that are less predictive of human reading times. First, regression analyses show a strictly monotonic, positive log-linear relationship between perplexity and fit to reading times for the more recently released five GPT-Neo variants and eight OPT variants on two separate datasets, replicating earlier results limited to just GPT-2 (Oh et al., 2022). Subsequently, analysis of residual errors reveals a systematic deviation of the larger variants, such as underpredicting reading times of named entities and making compensatory overpredictions for reading times of function words such as modals and conjunctions. These results suggest that the propensity of larger Transformer-based models to {`}memorize{'} sequences during training makes their surprisal estimates diverge from humanlike expectations, which warrants caution in using pre-trained language models to study human language processing.",
}

@misc{underwood_how_2020,
	title = {How predictable is fiction?},
	url = {https://tedunderwood.com/2020/07/05/how-predictable-is-fiction/},
	abstract = {This blog post is loosely connected to a talk I’m giving (virtually) at the Workshop on Narrative Understanding, Storylines, and Events at the ACL. It’s an informal talk, exploring some…},
	language = {en},
	urldate = {2023-12-11},
	journal = {The Stone and the Shell},
	author = {Underwood, Ted},
	month = jul,
	year = {2020},
	file = {Snapshot:/Users/au324704/Zotero/storage/8KNV6P3F/how-predictable-is-fiction.html:text/html},
}


@article{lassen2022reviewer,
  title={Reviewer Preferences and Gender Disparities in Aesthetic Judgments},
  author={Lassen, Ida Marie Schytt and Bizzoni, Yuri and Peura, Telam and Thomsen, Mads Rosendahl and Nielbo, Kristoffer Laigaard},
  journal={arXiv preprint arXiv:2206.08697},
  year={2022}
}


@inproceedings{dellorletta_is_2020,
	title = {Is {Neural} {Language} {Model} {Perplexity} {Related} to {Readability}?},
	isbn = {9791280136336},
	url = {http://books.openedition.org/aaccademia/8743},
	language = {en},
	urldate = {2023-11-27},
	booktitle = {Proceedings of the {Seventh} {Italian} {Conference} on {Computational} {Linguistics} {CLiC}-it 2020},
	publisher = {Accademia University Press},
	author = {Miaschi, Alessio and Alzetta, Chiara and Brunato, Dominique and Dell’Orletta, Felice and Venturi, Giulia},
	editor = {Dell'Orletta, Felice and Monti, Johanna and Tamburini, Fabio},
	year = {2020},
	doi = {10.4000/books.aaccademia.8743},
	pages = {303--309},
	annote = {so showing no relation between readability and perplexity -- but this might have to do with that they assess readability using the READ-IT (italian assessment tool) that makes readability a binary classificiation task (easy-hard)`???

they looked across various domains so narrative as well, websites etc.
},
	file = {Miaschi et al. - 2020 - Is Neural Language Model Perplexity Related to Rea.pdf:/Users/au324704/Zotero/storage/CP8QZ9LV/Miaschi et al. - 2020 - Is Neural Language Model Perplexity Related to Rea.pdf:application/pdf},
}


@inproceedings{goodkind_predictive_2018,
	address = {Salt Lake City, Utah},
	title = {Predictive power of word surprisal for reading times is a linear function of language model quality},
	url = {https://aclanthology.org/W18-0102},
	doi = {10.18653/v1/W18-0102},
	urldate = {2023-11-27},
	booktitle = {Proceedings of the 8th {Workshop} on {Cognitive} {Modeling} and {Computational} {Linguistics} ({CMCL} 2018)},
	publisher = {Association for Computational Linguistics},
	author = {Goodkind, Adam and Bicknell, Klinton},
	editor = {Sayeed, Asad and Jacobs, Cassandra and Linzen, Tal and van Schijndel, Marten},
	month = jan,
	year = {2018},
	pages = {10--18},
	file = {Full Text PDF:/Users/au324704/Zotero/storage/47GZCDVV/Goodkind and Bicknell - 2018 - Predictive power of word surprisal for reading tim.pdf:application/pdf},
}

@article{levy_expectation_based_2008,
	title = {Expectation-based syntactic comprehension},
	volume = {106},
	issn = {00100277},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0010027707001436},
	doi = {10.1016/j.cognition.2007.05.006},
	language = {en},
	number = {3},
	urldate = {2023-12-02},
	journal = {Cognition},
	author = {Levy, Roger},
	month = mar,
	year = {2008},
	pages = {1126--1177},
}


@article{balling_probability_2012,
	title = {Probability and surprisal in auditory comprehension of morphologically complex words},
	volume = {125},
	issn = {00100277},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0010027712001230},
	doi = {10.1016/j.cognition.2012.06.003},
	language = {en},
	number = {1},
	urldate = {2023-12-02},
	journal = {Cognition},
	author = {Balling, Laura Winther and Baayen, R. Harald},
	month = oct,
	year = {2012},
	pages = {80--106},
	file = {Submitted Version:/Users/au324704/Zotero/storage/7ZRMVC7F/Balling and Baayen - 2012 - Probability and surprisal in auditory comprehensio.pdf:application/pdf},
}

@inproceedings{hale_probabilistic_2001,
	title = {A {Probabilistic} {Earley} {Parser} as a {Psycholinguistic} {Model}},
	url = {https://aclanthology.org/N01-1021},
	urldate = {2023-12-02},
	booktitle = {Second {Meeting} of the {North} {American} {Chapter} of the {Association} for {Computational} {Linguistics}},
	author = {Hale, John},
	year = {2001},
	file = {Full Text PDF:/Users/au324704/Zotero/storage/UWKRI687/Hale - 2001 - A Probabilistic Earley Parser as a Psycholinguisti.pdf:application/pdf},
}

@inproceedings{bizzoni_good_2023,
	address = {Tórshavn, Faroe Islands},
	title = {Good {Reads} and {Easy} {Novels}: {Readability} and {Literary} {Quality} in a {Corpus} of {US}-published {Fiction}},
	shorttitle = {Good {Reads} and {Easy} {Novels}},
	url = {https://aclanthology.org/2023.nodalida-1.5},
	abstract = {In this paper, we explore the extent to which readability contributes to the perception of literary quality as defined by two categories of variables: expert-based (e.g., Pulitzer Prize, National Book Award) and crowd-based (e.g., GoodReads, WorldCat). Based on a large corpus of modern and contemporary fiction in English, we examine the correlation of a text's readability with its perceived literary quality, also assessing readability measures against simpler stylometric features. Our results show that readability generally correlates with popularity as measured through open platforms such as GoodReads and WorldCat but has an inverse relation with three prestigious literary awards. This points to a distinction between crowd- and expert-based judgments of literary style, as well as to a discrimination between fame and appreciation in the reception of a book.},
	urldate = {2023-11-23},
	booktitle = {Proceedings of the 24th {Nordic} {Conference} on {Computational} {Linguistics} ({NoDaLiDa})},
	publisher = {University of Tartu Library},
	author = {Bizzoni, Yuri and Moreira, Pascale and Dwenger, Nicole and Lassen, Ida and Thomsen, Mads and Nielbo, Kristoffer},
	editor = {Alumäe, Tanel and Fishel, Mark},
	month = may,
	year = {2023},
	pages = {42--51},
	file = {Full Text PDF:/Users/au324704/Zotero/storage/RD8PM7EN/Bizzoni et al. - 2023 - Good Reads and Easy Novels Readability and Litera.pdf:application/pdf},
}


@inproceedings{goodkind_predictive_2018,
	address = {Salt Lake City, Utah},
	title = {Predictive power of word surprisal for reading times is a linear function of language model quality},
	url = {https://aclanthology.org/W18-0102},
	doi = {10.18653/v1/W18-0102},
	urldate = {2023-11-27},
	booktitle = {Proceedings of the 8th {Workshop} on {Cognitive} {Modeling} and {Computational} {Linguistics} ({CMCL} 2018)},
	publisher = {Association for Computational Linguistics},
	author = {Goodkind, Adam and Bicknell, Klinton},
	editor = {Sayeed, Asad and Jacobs, Cassandra and Linzen, Tal and van Schijndel, Marten},
	month = jan,
	year = {2018},
	pages = {10--18},
	file = {Full Text PDF:/Users/au324704/Zotero/storage/47GZCDVV/Goodkind and Bicknell - 2018 - Predictive power of word surprisal for reading tim.pdf:application/pdf},
}


@inproceedings{gonzalez_analysis_2015,
	address = {Alicante, Spain},
	title = {An {Analysis} of {Twitter} {Corpora} and the {Diﬀerences} between {Formal} and {Colloquial} {Tweets}},
	abstract = {This work reviews recent publications addressing the Twitter translation task, and highlights the lack of appropriate corpora that represents the colloquial language used in Twitter. It also discusses the most well-know issues in the Twitter genre: the use of hashtags and the amount of OOVs, with especial focus in comparing the diﬀerences between formal and colloquial texts.},
	language = {en},
	booktitle = {"{Proceedings} of the {Tweet} {Translation} {Workshop} 2015"},
	publisher = {CEUR-WS.org},
	author = {Gonzalez, Meritxell},
	pages = {1--7},
    date = {2015},
	file = {Gonzalez - An Analysis of Twitter Corpora and the Diﬀerences .pdf:/Users/au324704/Zotero/storage/L3AEUR6Q/Gonzalez - An Analysis of Twitter Corpora and the Diﬀerences .pdf:application/pdf},
}



@article{bennett2003concept,
  title={The concept of a work in WorldCat: an application of FRBR},
  author={Bennett, Rick and Lavoie, Brian F and O’Neill, Edward T},
  journal={Library collections, acquisitions, and technical services},
  volume={27},
  number={1},
  pages={45--59},
  year={2003},
  publisher={Taylor \& Francis}
}


@inproceedings{bizzoni_sentimental_2023,
    title = "Sentimental Matters - Predicting Literary Quality by Sentiment Analysis and Stylometric Features",
    author = "Bizzoni, Yuri  and
      Moreira, Pascale  and
      Thomsen, Mads Rosendahl  and
      Nielbo, Kristoffer",
    booktitle = "Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, {\&} Social Media Analysis",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.wassa-1.2",
    pages = "11--18",
}


@article{liddle_could_2019,
	title = {Could Fiction Have an Information History? {Statistical Probability and the {Rise} of the {Novel}},
	shorttitle = {Could {Fiction} {Have} an {Information} {History}?},
	url = {https://culturalanalytics.org/article/11056},
	doi = {10.22148/16.033},
	urldate = {2023-11-22},
	journal = {Journal of Cultural Analytics},
	author = {Liddle, Dallas},
	year = {2019},
	file = {Liddle - 2019 - Could Fiction Have an Information History Statist.pdf:/Users/au324704/Zotero/storage/W3D99SJQ/Liddle - 2019 - Could Fiction Have an Information History Statist.pdf:application/pdf},
}

@article{mcgrath_measuring_2018,
	title = {Measuring {Modernist} {Novelty}},
	volume = {3},
	url = {https://culturalanalytics.org/article/11030},
	doi = {10.22148/16.027},
	number = {1},
	urldate = {2023-11-22},
	journal = {Journal of Cultural Analytics},
	author = {McGrath, Laura and Higgins, Devin and Hintze, Arend},
	year = {2018},
	file = {11030-measuring-modernist-novelty.pdf:/Users/au324704/Zotero/storage/X4M6HBP5/11030-measuring-modernist-novelty.pdf:application/pdf;Full Text:/Users/au324704/Zotero/storage/DQIXH2E3/McGrath et al. - 2018 - Measuring Modernist Novelty.pdf:application/pdf},
}


@incollection{baechler_information-theoretic_2016,
	title = {An information-theoretic approach to assess linguistic complexity},
	isbn = {978-3-11-034896-5},
	url = {https://www.degruyter.com/document/doi/10.1515/9783110348965-004/html},
	urldate = {2023-11-22},
	booktitle = {Complexity, {Isolation}, and {Variation}},
	publisher = {De Gruyter},
	author = {Ehret, Katharina and Szmrecsanyi, Benedikt},
	editor = {Baechler, Raffaela and Seiler, Guido},
	month = jul,
	year = {2016},
	doi = {10.1515/9783110348965-004},
	pages = {71--94},
}

@inproceedings{bizzoni-etal-2023-modeling,
    title = "Modeling Readers{'} Appreciation of Literary Narratives Through Sentiment Arcs and Semantic Profiles",
    author = "Bizzoni, Yuri  and
    Moreira, Pascale  and
      Nielbo, Kristoffer  and
      Lassen, Ida Marie  and
      Thomsen, Mads",
    booktitle = "Proceedings of the The 5th Workshop on Narrative Understanding",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.wnu-1.5",
    pages = "25--35",
    abstract = "Predicting literary quality and reader appreciation of narrative texts are highly complex challenges in quantitative and computational literary studies due to the fluid definitions of quality and the vast feature space that can be considered when modeling a literary work. This paper investigates the potential of sentiment arcs combined with topical-semantic profiling of literary narratives as indicators for their literary quality. Our experiments focus on a large corpus of 19th and 20the century English language literary fiction, using GoodReads{'} ratings as an imperfect approximation of the diverse range of reader evaluations and preferences. By leveraging a stacked ensemble of regression models, we achieve a promising performance in predicting average readers{'} scores, indicating the potential of our approach in modeling literary quality.",
}