Emotion Classification by Jointly Learning to Lexiconize and Classify
Deyu Zhou, Shuangzhi Wu, Qing Wang, Jun Xie, Zhaopeng Tu, Mu Li
Abstract
Emotion lexicons have been shown effective for emotion classification (Baziotis et al., 2018). Previous studies handle emotion lexicon construction and emotion classification separately. In this paper, we propose an emotional network (EmNet) to jointly learn sentence emotions and construct emotion lexicons which are dynamically adapted to a given context. The dynamic emotion lexicons are useful for handling words with multiple emotions based on different context, which can effectively improve the classification accuracy. We validate the approach on two representative architectures – LSTM and BERT, demonstrating its superiority on identifying emotions in Tweets. Our model outperforms several approaches proposed in previous studies and achieves new state-of-the-art on the benchmark Twitter dataset.
BibTeX
@inproceedings{zhou-etal-2020-emotion,
title = "Emotion Classification by Jointly Learning to Lexiconize and Classify",
author = "Zhou, Deyu and
Wu, Shuangzhi and
Wang, Qing and
Xie, Jun and
Tu, Zhaopeng and
Li, Mu",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.288/",
doi = "10.18653/v1/2020.coling-main.288",
pages = "3235--3245"
}