ACL 2025short0 citations
CHEER-Ekman: Fine-grained Embodied Emotion Classification
Phan Anh Duong, Cat Luong, Divyesh Bommana, Tianyu Jiang
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.
BibTeX
@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://aclanthology.org/2025.acl-short.88/",
doi = "10.18653/v1/2025.acl-short.88",
pages = "1118--1131",
ISBN = "979-8-89176-252-7"
}