@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",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T CHEER-Ekman: Fine-grained Embodied Emotion Classification
%A Duong, Phan Anh
%A Luong, Cat
%A Bommana, Divyesh
%A Jiang, Tianyu
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-252-7
%F duong-etal-2025-cheer
%X 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.
%R 10.18653/v1/2025.acl-short.88
%U https://aclanthology.org/2025.acl-short.88/
%U https://doi.org/10.18653/v1/2025.acl-short.88
%P 1118-1131
Markdown (Informal)
[CHEER-Ekman: Fine-grained Embodied Emotion Classification](https://aclanthology.org/2025.acl-short.88/) (Duong et al., ACL 2025)
ACL
- Phan Anh Duong, Cat Luong, Divyesh Bommana, and Tianyu Jiang. 2025. CHEER-Ekman: Fine-grained Embodied Emotion Classification. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 1118–1131, Vienna, Austria. Association for Computational Linguistics.