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

@article{russell1980circumplex,
  author    = {James A. Russell},
  title     = {A Circumplex Model of Affect},
  journal   = {Journal of Personality and Social Psychology},
  volume    = {39},
  number    = {6},
  pages     = {1161--1178},
  year      = {1980},
  doi       = {10.1037/h0077714}
}


@article{bradley1994sam,
  author    = {Margaret M. Bradley and Peter J. Lang},
  title     = {Measuring Emotion: The Self-Assessment Manikin and the Semantic Differential},
  journal   = {Journal of Behavior Therapy and Experimental Psychiatry},
  volume    = {25},
  number    = {1},
  pages     = {49--59},
  year      = {1994},
  doi       = {10.1016/0005-7916(94)90063-9}
}

@inproceedings{preotiucpietro2016valence,
  author    = {Daniel Preo\c{t}iuc-Pietro and H. Andrew Schwartz and Gregory Park and Johannes Eichstaedt and Margaret Kern and Lyle Ungar and Elisabeth Shulman},
  title     = {Modelling Valence and Arousal in {F}acebook Posts},
  booktitle = {Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis},
  pages     = {9--15},
  year      = {2016}
}

@inproceedings{buechel2016regression,
  author    = {Sven Buechel and Udo Hahn},
  title     = {Emotion Analysis as a Regression Problem -- Dimensional Models and Their Implications on Emotion Representation and Metrical Evaluation},
  booktitle = {Proceedings of the 22nd European Conference on Artificial Intelligence (ECAI 2016)},
  pages     = {1114--1122},
  year      = {2016},
  publisher = {IOS Press}
}



# Related Work
@inproceedings{10.1007/978-3-031-28244-7_6,
author = {Mendes, Gon\c{c}alo Azevedo and Martins, Bruno},
title = {Quantifying Valence and Arousal in Text with Multilingual Pre-trained Transformers},
year = {2023},
isbn = {978-3-031-28243-0},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
url = {https://doi.org/10.1007/978-3-031-28244-7_6},
doi = {10.1007/978-3-031-28244-7_6},
abstract = {The analysis of emotions expressed in text has numerous applications. In contrast to categorical analysis, focused on classifying emotions according to a pre-defined set of common classes, dimensional approaches can offer a more nuanced way to distinguish between different emotions. Still, dimensional methods have been less studied in the literature. Considering a valence-arousal dimensional space, this work assesses the use of pre-trained Transformers to predict these two dimensions on a continuous scale, with input texts from multiple languages and domains. We specifically combined multiple annotated datasets from previous studies, corresponding to either emotional lexica or short text documents, and evaluated models of multiple sizes and trained under different settings. Our results show that model size can have a significant impact on the quality of predictions, and that by fine-tuning a large model we can confidently predict valence and arousal in multiple languages. We make available the code, models, and supporting data.},
booktitle = {Advances in Information Retrieval: 45th European Conference on Information Retrieval, ECIR 2023, Dublin, Ireland, April 2–6, 2023, Proceedings, Part I},
pages = {84–100},
numpages = {17},
keywords = {Transformer-based multilingual language models, Emotion analysis in text, Predicting valence and arousal},
location = {Dublin, Ireland}
}


@misc{becker2026dimstancemultilingualdatasetsdimensional,
      title={DimStance: Multilingual Datasets for Dimensional Stance Analysis}, 
      author={Jonas Becker and Liang-Chih Yu and Shamsuddeen Hassan Muhammad and Jan Philip Wahle and Terry Ruas and Idris Abdulmumin and Lung-Hao Lee and Nelson Odhiambo and Lilian Wanzare and Wen-Ni Liu and Tzu-Mi Lin and Zhe-Yu Xu and Ying-Lung Lin and Jin Wang and Maryam Ibrahim Mukhtar and Bela Gipp and Saif M. Mohammad},
      year={2026},
      eprint={2601.21483},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2601.21483}, 
}

@article{lin2024enhanced,
author = {Lin, Diefan and Wen, Yi and Wang, Weishi and Su, Yan},
year = {2024},
month = {01},
pages = {1-1},
title = {Enhanced Sentiment Intensity Regression Through LoRA Fine-Tuning on Llama 3},
volume = {PP},
journal = {IEEE Access},
doi = {10.1109/ACCESS.2024.3438353}
}

@inproceedings{mitsios-etal-2024-improved,
    title = "Improved Text Emotion Prediction Using Combined Valence and Arousal Ordinal Classification",
    author = "Mitsios, Michail  and
      Vamvoukakis, Georgios  and
      Maniati, Georgia  and
      Ellinas, Nikolaos  and
      Dimitriou, Georgios  and
      Markopoulos, Konstantinos  and
      Kakoulidis, Panos  and
      Vioni, Alexandra  and
      Christidou, Myrsini  and
      Oh, Junkwang  and
      Jho, Gunu  and
      Hwang, Inchul  and
      Vardaxoglou, Georgios  and
      Chalamandaris, Aimilios  and
      Tsiakoulis, Pirros  and
      Raptis, Spyros",
    editor = "Duh, Kevin  and
      Gomez, Helena  and
      Bethard, Steven",
    booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.naacl-short.72/",
    doi = "10.18653/v1/2024.naacl-short.72",
    pages = "808--813",
    abstract = "Emotion detection in textual data has received growing interest in recent years, as it is pivotal for developing empathetic human-computer interaction systems.This paper introduces a method for categorizing emotions from text, which acknowledges and differentiates between the diversified similarities and distinctions of various emotions.Initially, we establish a baseline by training a transformer-based model for standard emotion classification, achieving state-of-the-art performance. We argue that not all misclassifications are of the same importance, as there are perceptual similarities among emotional classes.We thus redefine the emotion labeling problem by shifting it from a traditional classification model to an ordinal classification one, where discrete emotions are arranged in a sequential order according to their valence levels.Finally, we propose a method that performs ordinal classification in the two-dimensional emotion space, considering both valence and arousal scales.The results show that our approach not only preserves high accuracy in emotion prediction but also significantly reduces the magnitude of errors in cases of misclassification."
}

@misc{ahire2025mavenmultimodalattentionvalencearousal,
      title={MAVEN: Multi-modal Attention for Valence-Arousal Emotion Network}, 
      author={Vrushank Ahire and Kunal Shah and Mudasir Nazir Khan and Nikhil Pakhale and Lownish Rai Sookha and M. A. Ganaie and Abhinav Dhall},
      year={2025},
      eprint={2503.12623},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2503.12623}, 
}


@misc{ganesan2026wordsequencesbehavioralsequences,
      title={From Word Sequences to Behavioral Sequences: Adapting Modeling and Evaluation Paradigms for Longitudinal NLP}, 
      author={Adithya V Ganesan and Vasudha Varadarajan and Oscar NE Kjell and Whitney R Ringwald and Scott Feltman and Benjamin J Luft and Roman Kotov and Ryan L Boyd and H Andrew Schwartz},
      year={2026},
      eprint={2601.07988},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2601.07988}, 
}

@inproceedings{tseriotou-etal-2023-sequential,
    title = "Sequential Path Signature Networks for Personalised Longitudinal Language Modeling",
    author = "Tseriotou, Talia  and
      Tsakalidis, Adam  and
      Foster, Peter  and
      Lyons, Terence  and
      Liakata, Maria",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.310/",
    doi = "10.18653/v1/2023.findings-acl.310",
    pages = "5016--5031",
    abstract = "Longitudinal user modeling can provide a strong signal for various downstream tasks. Despite the rapid progress in representation learning, dynamic aspects of modelling individuals' language have only been sparsely addressed. We present a novel extension of neural sequential models using the notion of path signatures from rough path theory, which constitute graduated summaries of continuous paths and have the ability to capture non-linearities in trajectories. By combining path signatures of users' history with contextual neural representations and recursive neural networks we can produce compact time-sensitive user representations. Given the magnitude of mental health conditions with symptoms manifesting in language, we show the applicability of our approach on the task of identifying changes in individuals' mood by analysing their online textual content. By directly integrating signature transforms of users' history in the model architecture we jointly address the two most important aspects of the task, namely sequentiality and temporality. Our approach achieves state-of-the-art performance on macro-average F1 score on the two available datasets for the task, outperforming or performing on-par with state-of-the-art models utilising only historical posts and even outperforming prior models which also have access to future posts of users."
}

@inproceedings{10.1007/978-3-031-49896-1_19,
author = {Hills, Anthony and Tsakalidis, Adam and Liakata, Maria},
title = {Time-Aware Predictions of Moments of Change in Longitudinal User Posts on Social Media},
year = {2023},
isbn = {978-3-031-49895-4},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
url = {https://doi.org/10.1007/978-3-031-49896-1_19},
doi = {10.1007/978-3-031-49896-1_19},
abstract = {Capturing changes in an individual’s language is an important aspect of personalised mental health monitoring. A key component is modelling the influence of time, as contextual information both in the recent or distant past/future carries varying semantic weight. We capture and contrast this information by identifying neural, time-sensitive, bi-directional representations of individuals – modelling time-intervals in their social-media posts inspired by the Hawkes process. We demonstrate that our approach helps identify whether an individual’s mood is changing drastically, or smoothly on two social media datasets – yielding superior performance compared to time-insensitive baselines and outperforming the state-of-the-art on the CLPsych 2022 shared task.},
booktitle = {Advanced Analytics and Learning on Temporal Data: 8th ECML PKDD Workshop, AALTD 2023, Turin, Italy, September 18–22, 2023, Revised Selected Papers},
pages = {293–305},
numpages = {13},
keywords = {Longitudinal Modelling, Mental Health, Social Media},
location = {Turin, Italy}
}

@inproceedings{hills-etal-2024-exciting,
    title = "Exciting Mood Changes: A Time-aware Hierarchical Transformer for Change Detection Modelling",
    author = "Hills, Anthony  and
      Tseriotou, Talia  and
      Miscouridou, Xenia  and
      Tsakalidis, Adam  and
      Liakata, Maria",
    editor = "Ku, Lun-Wei  and
      Martins, Andre  and
      Srikumar, Vivek",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.findings-acl.744/",
    doi = "10.18653/v1/2024.findings-acl.744",
    pages = "12526--12537",
    abstract = "Through the rise of social media platforms, longitudinal language modelling has received much attention over the latest years, especially in downstream tasks such as mental health monitoring of individuals where modelling linguistic content in a temporal fashion is crucial. A key limitation in existing work is how to effectively model temporal sequences within Transformer-based language models. In this work we address this challenge by introducing a novel approach for predicting `Moments of Change' (MoC) in the mood of online users, by simultaneously considering user linguistic and time-aware context. A Hawkes process-inspired transformation layer is applied over the proposed architecture to model the influence of time on users' posts {--} capturing both their immediate and historical dynamics. We perform experiments on the two existing datasets for the MoC task and showcase clear performance gains when leveraging the proposed layer. Our ablation study reveals the importance of considering temporal dynamics in detecting subtle and rare mood changes. Our results indicate that considering linguistic and temporal information in a hierarchical manner provide valuable insights into the temporal dynamics of modelling user generated content over time, with applications in mental health monitoring."
}

@inproceedings{tripodi-etal-2025-assessing,
    title = "Assessing effective de-escalation of crisis conversations using transformer-based models and trend statistics",
    author = "Tripodi, Ignacio J.  and
      Buda, Greg  and
      Meagher, Margaret  and
      Olson, Elizabeth A.",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.emnlp-main.1512/",
    doi = "10.18653/v1/2025.emnlp-main.1512",
    pages = "29763--29777",
    ISBN = "979-8-89176-332-6",
    abstract = "One of the core goals of crisis counseling services is to support emotional de-escalation of the individual in crisis, by reducing intense negative emotional affect and emotional dysregulation. The science of crisis intervention has been impeded, however, by a lack of quantitative approaches that allow for detailed analysis of emotion in crisis conversations. In order to measure de-escalation at scale (millions of text-based conversations), lightweight models are needed that can assign not just binary sentiment predictions but quantitative scores to capture graded change in emotional valence. Accordingly, we developed a transformer-based emotional valence scoring model fit for crisis conversations, BERT-EV, that assigns numerical emotional valence scores to rate the intensity of expressed negative versus positive emotion. This transformer-based model can run on modest hardware configurations, allowing it to scale affordably and efficiently to a massive corpus of crisis conversations. We evaluated model performance on a corpus of hand-scored social media messages, and found that BERT-EV outperforms existing dictionary-based standard tools in the field, as well as other transformer-based implementations and an LLM in accurately matching scores from human annotators. Finally, we show that trends in these emotional valence scores can be used to assess emotional de-escalation during crisis conversations, with sufficient turn-by-turn granularity to help identify helpful vs. detrimental crisis counselor statements."
}


@article{TOMMASEL2021259,
title = {Forecasting mental health and emotions based on social media expressions during the COVID-19 pandemic},
journal = {Information Discovery and Delivery},
volume = {49},
number = {3},
pages = {259-268},
year = {2021},
issn = {2398-6247},
doi = {https://doi.org/10.1108/IDD-01-2021-0003},
url = {https://www.sciencedirect.com/science/article/pii/S2398624721000078},
author = {Antonela Tommasel and Andrés Diaz-Pace and Juan Manuel Rodriguez and Daniela Godoy},
keywords = {Social media, Time series forecasting, Mental health, COVID-19, Psycho-linguistic text analysis, Social analytics},
abstract = {Purpose
The purpose of this paper is to present an approach for forecasting mental health conditions and emotions of a given population during the COVID-19 pandemic in Argentina based on social media contents.
Design/methodology/approach
Mental health conditions and emotions are captured via markers, which link social media contents with lexicons. First, the authors build time series models that describe the evolution of markers and their correlation with crisis events. Second, the authors use the time series for forecasting markers and identifying high prevalence points for the estimated markers.
Findings
The authors evaluated different forecasting strategies that yielded different performance and capabilities. In the best scenario, high prevalence periods of emotions and mental health issues can be satisfactorily predicted with a neural network strategy, even at early stages of a crisis (e.g. a training period of seven days).
Practical implications
This work contributes to a better understanding of how psychological processes related to crises manifest in social media, and this is a valuable asset for the design, implementation and monitoring of health prevention and communication policies.
Originality/value
Although there have been previous efforts to predict mental states of individuals, the analysis of mental health at the collective level has received scarce attention. The authors take a step forward by proposing a forecasting approach for analyzing the mental health of a given population at a larger scale.}
}

@inproceedings{matero-schwartz-2020-autoregressive,
    title = "Autoregressive Affective Language Forecasting: A Self-Supervised Task",
    author = "Matero, Matthew  and
      Schwartz, H. Andrew",
    editor = "Scott, Donia  and
      Bel, Nuria  and
      Zong, Chengqing",
    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.261/",
    doi = "10.18653/v1/2020.coling-main.261",
    pages = "2913--2923",
    abstract = "Human natural language is mentioned at a specific point in time while human emotions change over time. While much work has established a strong link between language use and emotional states, few have attempted to model emotional language in time. Here, we introduce the task of \textit{affective language forecasting} {--} predicting future change in language based on past changes of language, a task with real-world applications such as treating mental health or forecasting trends in consumer confidence. We establish some of the fundamental autoregressive characteristics of the task (necessary history size, static versus dynamic length, varying time-step resolutions) and then build on popular sequence models for \textit{words} to instead model sequences of \textit{language-based emotion in time}. Over a novel Twitter dataset of 1,900 users and weekly + daily scores for 6 emotions and 2 additional linguistic attributes, we find a novel dual-sequence GRU model with decayed hidden states achieves best results ($r = .66$) significantly out-predicting, e.g., a moving averaging based on the past time-steps ($r = .49$). We make our anonymized dataset as well as task setup and evaluation code available for others to build on."
}


@article{Steyer1999,
  author  = {Steyer, Rolf and Schmitt, Manfred and Eid, Michael},
  title   = {Latent state-trait theory and research in personality and individual differences},
  journal = {European Journal of Personality},
  volume  = {13},
  number  = {5},
  pages   = {389--408},
  year    = {1999},
  doi     = {10.1002/(SICI)1099-0984(199909/10)13:5<389::AID-PER361>3.0.CO;2-A},
  url     = {https://journals.sagepub.com/doi/abs/10.1002/%28SICI%291099-0984%28199909/10%2913%3A5%3C389%3A%3AAID-PER361%3E3.0.CO%3B2-A}
}


@article{Wurpts2015,
  author  = {Wurpts, Ingrid C.},
  title   = {Performance of Contextual Multilevel Models for Comparing Between-Person and Within-Person Effects},
  journal = {Multivariate Behavioral Research},
  year    = {2015},
  volume  = {50},
  number  = {6},
  pages   = {721},
  doi     = {10.1080/00273171.2015.1120176},
  pmid    = {26717129},
  pmcid   = {PMC4836851}
}


@inproceedings{mireshghallah-etal-2022-useridentifier,
    title = "{U}ser{I}dentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis",
    author = "Mireshghallah, Fatemehsadat  and
      Shrivastava, Vaishnavi  and
      Shokouhi, Milad  and
      Berg-Kirkpatrick, Taylor  and
      Sim, Robert  and
      Dimitriadis, Dimitrios",
    editor = "Carpuat, Marine  and
      de Marneffe, Marie-Catherine  and
      Meza Ruiz, Ivan Vladimir",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.naacl-main.252/",
    doi = "10.18653/v1/2022.naacl-main.252",
    pages = "3449--3456",
    abstract = "Global models are typically trained to be as generalizable as possible. Invariance to the specific user is considered desirable since models are shared across multitudes of users. However, these models are often unable to produce personalized responses for individual users, based on their data. Contrary to widely-used personalization techniques based on few-shot and meta-learning, we propose UserIdentifier, a novel scheme for training a single shared model for all users. Our approach produces personalized responses by prepending a fixed, user-specific non-trainable string (called ``user identifier'') to each user{'}s input text. Unlike prior work, this method doesn{'}t need any additional model parameters, any extra rounds of personal few-shot learning or any change made to the vocabulary. We empirically study different types of user identifiers (numeric, alphanumeric, and also randomly generated) and demonstrate that, surprisingly, randomly generated user identifiers outperform the prefix-tuning based state-of-the-art approach by up to 13, on a suite of sentiment analysis datasets."
}

@misc{Harry2026StateBlind,
  author  = {Harry, Tamunotonye and Ngong, Ivoline and Nweke, Chima and Feng, Yuanyuan and Near, Joseph},
  title   = {Beyond Fixed Psychological Personas: State Beats Trait, but Language Models are State-Blind},
  year    = {2026},
  eprint  = {2601.15395},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  doi     = {10.48550/arXiv.2601.15395}
}
@inproceedings{soni-etal-2025-evaluation,
    title = "Evaluation of {LLM}s-based Hidden States as Author Representations for Psychological Human-Centered {NLP} Tasks",
    author = "Soni, Nikita  and
      Chitale, Pranav  and
      Singh, Khushboo  and
      Balasubramanian, Niranjan  and
      Schwartz, H. Andrew",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.findings-naacl.426/",
    doi = "10.18653/v1/2025.findings-naacl.426",
    pages = "7673--7682",
    ISBN = "979-8-89176-195-7",
    abstract = "Like most of NLP, models for human-centered NLP tasks{---}tasks attempting to assess author-level information{---}predominantly use rep-resentations derived from hidden states of Transformer-based LLMs. However, what component of the LM is used for the representation varies widely. Moreover, there is a need for Human Language Models (HuLMs) that implicitly model the author and provide a user-level hidden state. Here, we systematically evaluate different ways of representing documents and users using different LM and HuLM architectures to predict task outcomes as both dynamically changing states and averaged trait-like user-level attributes of valence, arousal, empathy, and distress. We find that representing documents as an average of the token hidden states performs the best generally. Further, while a user-level hidden state itself is rarely the best representation, we find its inclusion in the model strengthens token or document embeddings used to derive document- and user-level representations resulting in best performances."
}

@article{shu2024enhanced,
  author  = {Shu, Chang},
  title   = {Enhanced Personalized Text Generation Using User Embeddings and Attention Mechanisms},
  journal = {Applied and Computational Engineering},
  year    = {2024},
  volume  = {107},
  number  = {1},
  pages   = {61--72},
  month   = {11},
  doi     = {10.54254/2755-2721/107/20241355},
  url     = {https://doi.org/10.54254/2755-2721/107/20241355}
}


@misc{Schuurman2023WithinBetween,
  author  = {Schuurman, Noémi},
  title   = {A "Within/Between Problem" Primer: About (Not) Separating Within-Person Variance and Between-Person Variance in Psychology},
  year    = {2023},
  howpublished = {OSF Preprint},
  doi     = {10.31234/osf.io/7zgkx}
}

@article{Hoffman2009PersonsAsContexts,
  author  = {Hoffman, Lesa and Stawski, Robert S.},
  title   = {Persons as Contexts: Evaluating Between-Person and Within-Person Effects in Longitudinal Analysis},
  journal = {Research in Human Development},
  year    = {2009},
  volume  = {6},
  number  = {2-3},
  pages   = {97--120},
  doi     = {10.1080/15427600902911189}
}


@inproceedings{soni-etal-2026-semeval,
  title = "{S}em{E}val-2026 Task 2: Predicting Variation in Emotional Valence and Arousal over Time from Ecological Essays",
  author = "Soni, Nikita and
            Schwartz, H. Andrew and
            Boyd, Ryan L. and
            Bui, Phi Long and
            Mahwish, Syeda and
            Nilsson, August H{\aa}kan and
            Ganesan, Adithya V and
            Ungar, Lyle and
            Balasubramanian, Niranjan and
            Mohammad, Saif M.",
  booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)",
  year = "2026",
  publisher = "Association for Computational Linguistics"
}