Cascading Multiway Attentions for Document-level Sentiment Classification

Dehong Ma, Sujian Li, Xiaodong Zhang, Houfeng Wang, Xu Sun


Abstract
Document-level sentiment classification aims to assign the user reviews a sentiment polarity. Previous methods either just utilized the document content without consideration of user and product information, or did not comprehensively consider what roles the three kinds of information play in text modeling. In this paper, to reasonably use all the information, we present the idea that user, product and their combination can all influence the generation of attentions to words and sentences, when judging the sentiment of a document. With this idea, we propose a cascading multiway attention (CMA) model, where multiple ways of using user and product information are cascaded to influence the generation of attentions on the word and sentence layers. Then, sentences and documents are well modeled by multiple representation vectors, which provide rich information for sentiment classification. Experiments on IMDB and Yelp datasets demonstrate the effectiveness of our model.
Anthology ID:
I17-1064
Volume:
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
November
Year:
2017
Address:
Taipei, Taiwan
Editors:
Greg Kondrak, Taro Watanabe
Venue:
IJCNLP
SIG:
Publisher:
Asian Federation of Natural Language Processing
Note:
Pages:
634–643
Language:
URL:
https://aclanthology.org/I17-1064
DOI:
Bibkey:
Cite (ACL):
Dehong Ma, Sujian Li, Xiaodong Zhang, Houfeng Wang, and Xu Sun. 2017. Cascading Multiway Attentions for Document-level Sentiment Classification. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 634–643, Taipei, Taiwan. Asian Federation of Natural Language Processing.
Cite (Informal):
Cascading Multiway Attentions for Document-level Sentiment Classification (Ma et al., IJCNLP 2017)
Copy Citation:
PDF:
https://aclanthology.org/I17-1064.pdf
Data
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