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.- Anthology ID:
- 2024.cmcl-1.23
- Volume:
- Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Tatsuki Kuribayashi, Giulia Rambelli, Ece Takmaz, Philipp Wicke, Yohei Oseki
- Venues:
- CMCL | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 264–277
- Language:
- URL:
- https://aclanthology.org/2024.cmcl-1.23
- DOI:
- 10.18653/v1/2024.cmcl-1.23
- Cite (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.
- Cite (Informal):
- Improving Language Models for Emotion Analysis: Insights from Cognitive Science (Bonard & Cortal, CMCL-WS 2024)
- PDF:
- https://preview.aclanthology.org/autopr/2024.cmcl-1.23.pdf