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