Uncovering the Limits of Text-based Emotion Detection
Nurudin Alvarez-Gonzalez, Andreas Kaltenbrunner, Vicenç Gómez
Abstract
Identifying emotions from text is crucial for a variety of real world tasks. We consider the two largest now-available corpora for emotion classification: GoEmotions, with 58k messages labelled by readers, and Vent, with 33M writer-labelled messages. We design a benchmark and evaluate several feature spaces and learning algorithms, including two simple yet novel models on top of BERT that outperform previous strong baselines on GoEmotions. Through an experiment with human participants, we also analyze the differences between how writers express emotions and how readers perceive them. Our results suggest that emotions expressed by writers are harder to identify than emotions that readers perceive. We share a public web interface for researchers to explore our models.
BibTeX
@inproceedings{alvarez-gonzalez-etal-2021-uncovering-limits,
title = "Uncovering the Limits of Text-based Emotion Detection",
author = "Alvarez-Gonzalez, Nurudin and
Kaltenbrunner, Andreas and
G{\'o}mez, Vicen{\c{c}}",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.219/",
doi = "10.18653/v1/2021.findings-emnlp.219",
pages = "2560--2583"
}