Improving Language Models for Emotion Analysis: Insights from Cognitive Science
<|reference_start|>Improving Language Models for Emotion Analysis: Insights from Cognitive Science: 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.<|reference_end|>
@article{bonard2024improving,
    title={Improving Language Models for Emotion Analysis: Insights from Cognitive
  Science},
    author={Constant Bonard (UNIBE), Gustave Cortal (LMF, LISN)},
    journal={Proceedings of the Workshop on Cognitive Modeling and
  Computational Linguistics, Association for Computational Linguistics, Aug
  2024, Bangkok, Thailand. pp.264-277},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.10265},
    primaryClass={cs.CL cs.AI}
}