CancerEmo: A Dataset for Fine-Grained Emotion Detection

Tiberiu Sosea, Cornelia Caragea


Abstract
Emotions are an important element of human nature, often affecting the overall wellbeing of a person. Therefore, it is no surprise that the health domain is a valuable area of interest for emotion detection, as it can provide medical staff or caregivers with essential information about patients. However, progress on this task has been hampered by the absence of large labeled datasets. To this end, we introduce CancerEmo, an emotion dataset created from an online health community and annotated with eight fine-grained emotions. We perform a comprehensive analysis of these emotions and develop deep learning models on the newly created dataset. Our best BERT model achieves an average F1 of 71%, which we improve further using domain-specific pre-training.
Anthology ID:
2020.emnlp-main.715
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8892–8904
Language:
URL:
https://www.aclweb.org/anthology/2020.emnlp-main.715
DOI:
10.18653/v1/2020.emnlp-main.715
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.emnlp-main.715.pdf