Improving Language Models for Emotion Analysis: Insights from Cognitive Science
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909295590768640 |
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| author | Bonard, Constant Cortal, Gustave |
| author_facet | Bonard, Constant Cortal, Gustave |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_10265 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improving Language Models for Emotion Analysis: Insights from Cognitive Science Bonard, Constant Cortal, Gustave Computation and Language Artificial Intelligence 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. |
| title | Improving Language Models for Emotion Analysis: Insights from Cognitive Science |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2406.10265 |