@inproceedings{duong-etal-2025-cheer,
    title = "{CHEER}-{E}kman: Fine-grained Embodied Emotion Classification",
    author = "Duong, Phan Anh  and
      Luong, Cat  and
      Bommana, Divyesh  and
      Jiang, Tianyu",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.acl-short.88/",
    doi = "10.18653/v1/2025.acl-short.88",
    pages = "1118--1131",
    ISBN = "979-8-89176-252-7",
    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."
}