    

@article{mymodel,
  title={Emotion {C}lassification with {SpaCy} v3 & {C}omet},
  author={Kömeçoğlu, Buluz, Başak},
  year={2023},
  url={https://heartbeat.comet.ml/emotion-classification-with-spacy-v3-comet-eaff310c0d7c}
}          

@article{additionaldata,
  title={Emotion {D}etection in {T}ext using {N}atural {L}anguage {P}rocessing},
  author={Nikam, Sanket},
  year={n.d.},
  url={https://github.com/SannketNikam/Emotion-Detection-in-Text}
}          


@article{nextsentenceprediction,
  title={Next sentence prediction with {BERT}},
  author={Cathrine, Jeeva},
  journal={scaler},
  year={2023},
  url={https://www.scaler.com/topics/nlp/bert-next-sentence-prediction/}
}          


@ARTICLE{8764449,
  author={Poria, Soujanya and Majumder, Navonil and Mihalcea, Rada and Hovy, Eduard},
  journal={IEEE Access}, 
  title={Emotion {R}ecognition in {C}onversation: {R}esearch {C}hallenges, {D}atasets, and {R}ecent {A}dvances}, 
  year={2019},
  volume={7},
  number={},
  pages={100943-100953},
  keywords={Emotion recognition;Task analysis;Context modeling;Taxonomy;Natural language processing;Pragmatics;Emotion recognition;sentiment analysis;dialogue systems;natural language processing},
  doi={10.1109/ACCESS.2019.2929050}}



@Article{s21041322,
AUTHOR = {Graterol, Wilfredo and Diaz-Amado, Jose and Cardinale, Yudith and Dongo, Irvin and Lopes-Silva, Edmundo and Santos-Libarino, Cleia},
TITLE = {Emotion {D}etection for {S}ocial {R}obots {B}ased on {NLP} {T}ransformers and an {E}motion {O}ntology},
JOURNAL = {Sensors},
VOLUME = {21},
YEAR = {2021},
NUMBER = {4},
ARTICLE-NUMBER = {1322},
URL = {https://www.mdpi.com/1424-8220/21/4/1322},
PubMedID = {33668412},
ISSN = {1424-8220},
ABSTRACT = {For social robots, knowledge regarding human emotional states is an essential part of adapting their behavior or associating emotions to other entities. Robots gather the information from which emotion detection is processed via different media, such as text, speech, images, or videos. The multimedia content is then properly processed to recognize emotions/sentiments, for example, by analyzing faces and postures in images/videos based on machine learning techniques or by converting speech into text to perform emotion detection with natural language processing (NLP) techniques. Keeping this information in semantic repositories offers a wide range of possibilities for implementing smart applications. We propose a framework to allow social robots to detect emotions and to store this information in a semantic repository, based on EMONTO (an EMotion ONTOlogy), and in the first figure or table caption. Please define if appropriate. an ontology to represent emotions. As a proof-of-concept, we develop a first version of this framework focused on emotion detection in text, which can be obtained directly as text or by converting speech to text. We tested the implementation with a case study of tour-guide robots for museums that rely on a speech-to-text converter based on the Google Application Programming Interface (API) and a Python library, a neural network to label the emotions in texts based on NLP transformers, and EMONTO integrated with an ontology for museums; thus, it is possible to register the emotions that artworks produce in visitors. We evaluate the classification model, obtaining equivalent results compared with a state-of-the-art transformer-based model and with a clear roadmap for improvement.},
DOI = {10.3390/s21041322}
}





@article{gaind2019emotion,
  title={Emotion detection and analysis on social media},
  author={Gaind, Bharat and Syal, Varun and Padgalwar, Sneha},
  journal={arXiv preprint arXiv:1901.08458},
  year={2019}
}

@article{brynielsson2014emotion,
  title={Emotion classification of social media posts for estimating people’s reactions to communicated alert messages during crises},
  author={Brynielsson, Joel and Johansson, Fredrik and Jonsson, Carl and Westling, Anders},
  journal={Security Informatics},
  volume={3},
  pages={1--11},
  year={2014},
  publisher={Springer}
}


@inproceedings{fan2019knowledge,
  title={A knowledge regularized hierarchical approach for emotion cause analysis},
  author={Fan, Chuang and Yan, Hongyu and Du, Jiachen and Gui, Lin and Bing, Lidong and Yang, Min and Xu, Ruifeng and Mao, Ruibin},
  booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
  pages={5618--5628},
  year={2019},
  organization={Association for Computational Linguistics}
}

@inproceedings{ding2020end,
  title={End-to-end emotion-cause pair extraction based on sliding window multi-label learning},
  author={Ding, Zixiang and Xia, Rui and Yu, Jianfei},
  booktitle={Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP)},
  pages={3574--3583},
  year={2020}
}








@article{belpaeme2018social,
  title={Social robots for education: A review},
  author={Belpaeme, Tony and Kennedy, James and Ramachandran, Aditi and Scassellati, Brian and Tanaka, Fumihide},
  journal={Science robotics},
  volume={3},
  number={21},
  pages={eaat5954},
  year={2018},
  publisher={American Association for the Advancement of Science}
}

@article{broekens2009assistive,
  title={Assistive social robots in elderly care: a review},
  author={Broekens, Joost and Heerink, Marcel and Rosendal, Henk and others},
  journal={Gerontechnology},
  volume={8},
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  pages={94--103},
  year={2009}
}



@InProceedings{wang2022multimodal,
  author = {Wang, Fanfan  and  Ma, Heqing  and  Xia, Rui  and  Yu, Jianfei  and  Cambria, Erik},
  title = {SemEval-2024 Task 3: Multimodal Emotion Cause Analysis in Conversations},
  booktitle = {Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)},
  month = {June},
  year = {2024},
  address = {Mexico City, Mexico},
  publisher = {Association for Computational Linguistics},
  pages = {2022--2033},
  url = {https://aclanthology.org/2024.semeval2024-1.273}
}

@ARTICLE{wang2023multimodal,
author={Wang, Fanfan and Ding, Zixiang and Xia, Rui and Li, Zhaoyu and Yu, Jianfei},
journal={IEEE Transactions on Affective Computing}, 
title={Multimodal Emotion-Cause Pair Extraction in Conversations}, 
year={2023},
volume={14},
number={3},
pages={1832-1844},
}



@inproceedings{semeval2024task10data,

    title = "SemEval 2024 -- Task 10: Emotion Discovery and Reasoning its Flip in Conversation (EDiReF)",

    author = "Kumar, Shivani  and

      Akhtar, Md Shad  and

      Cambria, Erik and

      Chakraborty, Tanmoy",

    booktitle = "Proceedings of the 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics",

    year = "2024",

    publisher = "Association for Computational Linguistics",

    url = "https://arxiv.org/abs/2402.18944",

}


@inproceedings{task10dataset,
    title = "From Multilingual Complexity to Emotional Clarity: Leveraging Commonsense to Unveil Emotions in Code-Mixed Dialogues",
    author = "Kumar, Shivani  and
      S, Ramaneswaran  and
      Akhtar, Md  and
      Chakraborty, Tanmoy",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.emnlp-main.598",
    doi = "10.18653/v1/2023.emnlp-main.598",
    pages = "9638--9652",
    abstract = "Understanding emotions during conversation is a fundamental aspect of human communication, driving NLP research for Emotion Recognition in Conversation (ERC). While considerable research has focused on discerning emotions of individual speakers in monolingual dialogues, understanding the emotional dynamics in code-mixed conversations has received relatively less attention. This motivates our undertaking of ERC for code-mixed conversations in this study. Recognizing that emotional intelligence encompasses a comprehension of worldly knowledge, we propose an innovative approach that integrates commonsense information with dialogue context to facilitate a deeper understanding of emotions. To achieve this, we devise an efficient pipeline that extracts relevant commonsense from existing knowledge graphs based on the code-mixed input. Subsequently, we develop an advanced fusion technique that seamlessly combines the acquired commonsense information with the dialogue!
  representation obtained from a dedicated dialogue understanding module. Our comprehensive experimentation showcases the substantial performance improvement obtained through the systematic incorporation of commonsense in ERC. Both quantitative assessments and qualitative analyses further corroborate the validity of our hypothesis, reaffirming the pivotal role of commonsense integration in enhancing ERC.",
}