Improving Language Models for Emotion Analysis: Insights from Cognitive Science - Laboratoire Interdisciplinaire des Sciences du Numérique Access content directly
Conference Papers Year : 2024

Improving Language Models for Emotion Analysis: Insights from Cognitive Science

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.
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Dates and versions

hal-04624340 , version 1 (25-06-2024)
hal-04624340 , version 2 (03-07-2024)

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Constant Bonard, Gustave Cortal. Improving Language Models for Emotion Analysis: Insights from Cognitive Science. The 13th edition of the Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2024) co-located with the 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024), Aug 2024, Bangkok, Thailand. ⟨hal-04624340v2⟩
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