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        <title>Improving Language Models for Emotion Analysis: Insights from Cognitive Science</title>
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    <name type="personal">
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        <name type="personal">
            <namePart type="given">Tatsuki</namePart>
            <namePart type="family">Kuribayashi</namePart>
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            <namePart type="given">Giulia</namePart>
            <namePart type="family">Rambelli</namePart>
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        <name type="personal">
            <namePart type="given">Ece</namePart>
            <namePart type="family">Takmaz</namePart>
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        <name type="personal">
            <namePart type="given">Yohei</namePart>
            <namePart type="family">Oseki</namePart>
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    <abstract>We propose leveraging cognitive science research on emotions and communication to improve language models for emotion analysis. First, we present the main emotion theories in psychology and cognitive science. Then, we introduce the main methods of emotion annotation in natural language processing and their connections to psychological theories. We also present the two main types of analyses of emotional communication in cognitive pragmatics. Finally, based on the cognitive science research presented, we propose directions for improving language models for emotion analysis. We suggest that these research efforts pave the way for constructing new annotation schemes, methods, and a possible benchmark for emotional understanding, considering different facets of human emotion and communication.</abstract>
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    <identifier type="doi">10.18653/v1/2024.cmcl-1.23</identifier>
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%0 Conference Proceedings
%T Improving Language Models for Emotion Analysis: Insights from Cognitive Science
%A Bonard, Constant
%A Cortal, Gustave
%Y Kuribayashi, Tatsuki
%Y Rambelli, Giulia
%Y Takmaz, Ece
%Y Wicke, Philipp
%Y Oseki, Yohei
%S Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F bonard-cortal-2024-improving
%X We propose leveraging cognitive science research on emotions and communication to improve language models for emotion analysis. First, we present the main emotion theories in psychology and cognitive science. Then, we introduce the main methods of emotion annotation in natural language processing and their connections to psychological theories. We also present the two main types of analyses of emotional communication in cognitive pragmatics. Finally, based on the cognitive science research presented, we propose directions for improving language models for emotion analysis. We suggest that these research efforts pave the way for constructing new annotation schemes, methods, and a possible benchmark for emotional understanding, considering different facets of human emotion and communication.
%R 10.18653/v1/2024.cmcl-1.23
%U https://aclanthology.org/2024.cmcl-1.23
%U https://doi.org/10.18653/v1/2024.cmcl-1.23
%P 264-277

Markdown (Informal)

[Improving Language Models for Emotion Analysis: Insights from Cognitive Science](https://aclanthology.org/2024.cmcl-1.23) (Bonard & Cortal, CMCL-WS 2024)

• Improving Language Models for Emotion Analysis: Insights from Cognitive Science (Bonard & Cortal, CMCL-WS 2024)

ACL

• Constant Bonard and Gustave Cortal. 2024. Improving Language Models for Emotion Analysis: Insights from Cognitive Science. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, pages 264–277, Bangkok, Thailand. Association for Computational Linguistics.