CHEER-Ekman: Fine-grained Embodied Emotion Classification

• URL: http://arxiv.org/abs/2506.01047v2
• Date: Tue, 03 Jun 2025 03:33:33 GMT
• Title: CHEER-Ekman: Fine-grained Embodied Emotion Classification
• Authors: Phan Anh Duong, Cat Luong, Divyesh Bommana, Tianyu Jiang,
• Abstract summary: We extend the existing binary embodied emotion dataset with Ekman's six basic emotion categories.<n>Using automatic best-worst scaling with large language models, we achieve performance superior to supervised approaches.<n>Our investigation reveals that simplified prompting instructions and chain-of-thought reasoning significantly improve emotion recognition accuracy.
• Score: 4.762323642506733
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Emotions manifest through physical experiences and bodily reactions, yet identifying such embodied emotions in text remains understudied. We present an embodied emotion classification dataset, CHEER-Ekman, extending the existing binary embodied emotion dataset with Ekman's six basic emotion categories. Using automatic best-worst scaling with large language models, we achieve performance superior to supervised approaches on our new dataset. Our investigation reveals that simplified prompting instructions and chain-of-thought reasoning significantly improve emotion recognition accuracy, enabling smaller models to achieve competitive performance with larger ones. Our dataset is publicly available at: https://github.com/menamerai/cheer-ekman.

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