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    <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.</abstract>
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%0 Conference Proceedings
%T Emotion Classification by Jointly Learning to Lexiconize and Classify
%A Zhou, Deyu
%A Wu, Shuangzhi
%A Wang, Qing
%A Xie, Jun
%A Tu, Zhaopeng
%A Li, Mu
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F zhou-etal-2020-emotion
%X 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.
%R 10.18653/v1/2020.coling-main.288
%U https://aclanthology.org/2020.coling-main.288
%U https://doi.org/10.18653/v1/2020.coling-main.288
%P 3235-3245

Markdown (Informal)

[Emotion Classification by Jointly Learning to Lexiconize and Classify](https://aclanthology.org/2020.coling-main.288) (Zhou et al., COLING 2020)

• Emotion Classification by Jointly Learning to Lexiconize and Classify (Zhou et al., COLING 2020)

ACL

• Deyu Zhou, Shuangzhi Wu, Qing Wang, Jun Xie, Zhaopeng Tu, and Mu Li. 2020. Emotion Classification by Jointly Learning to Lexiconize and Classify. In Proceedings of the 28th International Conference on Computational Linguistics, pages 3235–3245, Barcelona, Spain (Online). International Committee on Computational Linguistics.