A Lexicon-Based Supervised Attention Model for Neural Sentiment Analysis

Yicheng Zou, Tao Gui, Qi Zhang, Xuanjing Huang


Abstract
Attention mechanisms have been leveraged for sentiment classification tasks because not all words have the same importance. However, most existing attention models did not take full advantage of sentiment lexicons, which provide rich sentiment information and play a critical role in sentiment analysis. To achieve the above target, in this work, we propose a novel lexicon-based supervised attention model (LBSA), which allows a recurrent neural network to focus on the sentiment content, thus generating sentiment-informative representations. Compared with general attention models, our model has better interpretability and less noise. Experimental results on three large-scale sentiment classification datasets showed that the proposed method outperforms previous methods.
Anthology ID:
C18-1074
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Editors:
Emily M. Bender, Leon Derczynski, Pierre Isabelle
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
868–877
Language:
URL:
https://aclanthology.org/C18-1074
DOI:
Bibkey:
Cite (ACL):
Yicheng Zou, Tao Gui, Qi Zhang, and Xuanjing Huang. 2018. A Lexicon-Based Supervised Attention Model for Neural Sentiment Analysis. In Proceedings of the 27th International Conference on Computational Linguistics, pages 868–877, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
A Lexicon-Based Supervised Attention Model for Neural Sentiment Analysis (Zou et al., COLING 2018)
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PDF:
https://aclanthology.org/C18-1074.pdf
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