CHEER-Ekman: Fine-grained Embodied Emotion Classification
This work addresses the understudied problem of fine-grained embodied emotion classification for researchers in natural language processing and affective computing, though it is incremental as it builds on an existing dataset.
The authors tackled the problem of identifying embodied emotions in text by creating a new dataset, CHEER-Ekman, which extends existing binary categories to Ekman's six basic emotions, and achieved superior performance using automatic best-worst scaling with large language models.
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.