@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",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Uncovering the Limits of Text-based Emotion Detection
%A Alvarez-Gonzalez, Nurudin
%A Kaltenbrunner, Andreas
%A Gómez, Vicenç
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F alvarez-gonzalez-etal-2021-uncovering-limits
%X 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.
%R 10.18653/v1/2021.findings-emnlp.219
%U https://aclanthology.org/2021.findings-emnlp.219
%U https://doi.org/10.18653/v1/2021.findings-emnlp.219
%P 2560-2583
Markdown (Informal)
[Uncovering the Limits of Text-based Emotion Detection](https://aclanthology.org/2021.findings-emnlp.219) (Alvarez-Gonzalez et al., Findings 2021)
ACL
- Nurudin Alvarez-Gonzalez, Andreas Kaltenbrunner, and Vicenç Gómez. 2021. Uncovering the Limits of Text-based Emotion Detection. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2560–2583, Punta Cana, Dominican Republic. Association for Computational Linguistics.