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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.01047 |
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| _version_ | 1866916968814084096 |
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| author | Duong, Phan Anh Luong, Cat Bommana, Divyesh Jiang, Tianyu |
| author_facet | Duong, Phan Anh Luong, Cat Bommana, Divyesh Jiang, Tianyu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_01047 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CHEER-Ekman: Fine-grained Embodied Emotion Classification Duong, Phan Anh Luong, Cat Bommana, Divyesh Jiang, Tianyu Computation and Language 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. |
| title | CHEER-Ekman: Fine-grained Embodied Emotion Classification |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2506.01047 |