Improving Language Models for Emotion Analysis: Insights from Cognitive Science
This work addresses the challenge of improving emotion analysis in NLP for applications like human-computer interaction, though it is incremental as it builds on existing theories without presenting new experimental results.
The paper tackles the problem of enhancing language models for emotion analysis by integrating insights from cognitive science, proposing new directions for annotation schemes, methods, and benchmarks to better capture human emotion and communication.
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.