Integrating Plutchik's Theory with Mixture of Experts for Enhancing Emotion Classification

ACL ARR 2024 June Submission5202 Authors

Abstract:

Emotion significantly influences human behavior and decision-making processes. We propose a labeling methodology grounded in Plutchik's Wheel of Emotions theory for emotion classification. Furthermore, we employ a Mixture of Experts (MoE) architecture to evaluate the efficacy of this labeling approach, by identifying the specific emotions that each expert learns to classify. Experimental results reveal that our methodology improves the performance of emotion classification.

Paper Type: Long

Research Area: Computational Social Science and Cultural Analytics

Research Area Keywords: emotion detection and analysis

Contribution Types: Model analysis & interpretability, NLP engineering experiment

Languages Studied: English

Submission Number: 5202