A Variational Approach to Weakly Supervised Document-Level Multi-Aspect Sentiment Classification

Ziqian Zeng, Wenxuan Zhou, Xin Liu, Yangqiu Song

Abstract
In this paper, we propose a variational approach to weakly supervised document-level multi-aspect sentiment classification. Instead of using user-generated ratings or annotations provided by domain experts, we use target-opinion word pairs as “supervision.” These word pairs can be extracted by using dependency parsers and simple rules. Our objective is to predict an opinion word given a target word while our ultimate goal is to learn a sentiment polarity classifier to predict the sentiment polarity of each aspect given a document. By introducing a latent variable, i.e., the sentiment polarity, to the objective function, we can inject the sentiment polarity classifier to the objective via the variational lower bound. We can learn a sentiment polarity classifier by optimizing the lower bound. We show that our method can outperform weakly supervised baselines on TripAdvisor and BeerAdvocate datasets and can be comparable to the state-of-the-art supervised method with hundreds of labels per aspect.
Anthology ID:
N19-1036
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Editors:
Jill Burstein, Christy Doran, Thamar Solorio
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
386–396
Language:
URL:
https://aclanthology.org/N19-1036
DOI:
10.18653/v1/N19-1036
Bibkey:
Cite (ACL):
Ziqian Zeng, Wenxuan Zhou, Xin Liu, and Yangqiu Song. 2019. A Variational Approach to Weakly Supervised Document-Level Multi-Aspect Sentiment Classification. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 386–396, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
A Variational Approach to Weakly Supervised Document-Level Multi-Aspect Sentiment Classification (Zeng et al., NAACL 2019)
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PDF:
https://preview.aclanthology.org/naacl24-info/N19-1036.pdf
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 HKUST-KnowComp/VWS-DMSC
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