Improving Language Models for Emotion Analysis: Insights from Cognitive Science

TL;DR

This paper integrates cognitive science insights into emotion theories and communication to enhance language models' ability to analyze emotions, proposing new annotation schemes and benchmarks for emotional understanding.

Contribution

It introduces a framework combining psychological theories and NLP methods to improve emotion analysis in language models, suggesting new directions for research and evaluation.

Findings

Connection between emotion theories and NLP annotation methods

Proposal of new annotation schemes for emotional data

Directions for developing emotion benchmarks

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.