@inproceedings{sosea-caragea-2020-canceremo,
title = "{C}ancer{E}mo: A Dataset for Fine-Grained Emotion Detection",
author = "Sosea, Tiberiu and
Caragea, Cornelia",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.715",
doi = "10.18653/v1/2020.emnlp-main.715",
pages = "8892--8904",
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.",
}
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%0 Conference Proceedings
%T CancerEmo: A Dataset for Fine-Grained Emotion Detection
%A Sosea, Tiberiu
%A Caragea, Cornelia
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F sosea-caragea-2020-canceremo
%X 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.
%R 10.18653/v1/2020.emnlp-main.715
%U https://aclanthology.org/2020.emnlp-main.715
%U https://doi.org/10.18653/v1/2020.emnlp-main.715
%P 8892-8904
Markdown (Informal)
[CancerEmo: A Dataset for Fine-Grained Emotion Detection](https://aclanthology.org/2020.emnlp-main.715) (Sosea & Caragea, EMNLP 2020)
ACL