Improving Language Models for Emotion Analysis: Insights from Cognitive Science - Laboratoire Interdisciplinaire des Sciences du Numérique
Communication Dans Un Congrès Année : 2024

Improving Language Models for Emotion Analysis: Insights from Cognitive Science

Résumé

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.
Fichier principal
Vignette du fichier
CMCL_ACL_2024.pdf (302.74 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04624340 , version 1 (25-06-2024)
hal-04624340 , version 2 (03-07-2024)
hal-04624340 , version 3 (26-08-2024)

Identifiants

Citer

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⟩
192 Consultations
64 Téléchargements

Altmetric

Partager

More