Making the Best Use of Review Summary for Sentiment Analysis

Sen Yang, Leyang Cui, Jun Xie, Yue Zhang


Abstract
Sentiment analysis provides a useful overview of customer review contents. Many review websites allow a user to enter a summary in addition to a full review. Intuitively, summary information may give additional benefit for review sentiment analysis. In this paper, we conduct a study to exploit methods for better use of summary information. We start by finding out that the sentimental signal distribution of a review and that of its corresponding summary are in fact complementary to each other. We thus explore various architectures to better guide the interactions between the two and propose a hierarchically-refined review-centric attention model. Empirical results show that our review-centric model can make better use of user-written summaries for review sentiment analysis, and is also more effective compared to existing methods when the user summary is replaced with summary generated by an automatic summarization system.
Anthology ID:
2020.coling-main.15
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
173–184
Language:
URL:
https://www.aclweb.org/anthology/2020.coling-main.15
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.coling-main.15.pdf