CHEER-Ekman: Fine-grained Embodied Emotion Classification

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:this https URL.

@article{duong2025_2506.01047,
  title={ CHEER-Ekman: Fine-grained Embodied Emotion Classification },
  author={ Phan Anh Duong and Cat Luong and Divyesh Bommana and Tianyu Jiang },
  journal={arXiv preprint arXiv:2506.01047},
  year={ 2025 }
}