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        <title>Making the Best Use of Review Summary for Sentiment Analysis</title>
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        <namePart type="given">Sen</namePart>
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            <title>Proceedings of the 28th International Conference on Computational Linguistics</title>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2020.coling-main.15</identifier>
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        <date>2020-12</date>
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%0 Conference Proceedings
%T Making the Best Use of Review Summary for Sentiment Analysis
%A Yang, Sen
%A Cui, Leyang
%A Xie, Jun
%A Zhang, Yue
%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 yang-etal-2020-making
%X 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.
%R 10.18653/v1/2020.coling-main.15
%U https://aclanthology.org/2020.coling-main.15
%U https://doi.org/10.18653/v1/2020.coling-main.15
%P 173-184

Markdown (Informal)

[Making the Best Use of Review Summary for Sentiment Analysis](https://aclanthology.org/2020.coling-main.15) (Yang et al., COLING 2020)

• Making the Best Use of Review Summary for Sentiment Analysis (Yang et al., COLING 2020)

ACL

• Sen Yang, Leyang Cui, Jun Xie, and Yue Zhang. 2020. Making the Best Use of Review Summary for Sentiment Analysis. In Proceedings of the 28th International Conference on Computational Linguistics, pages 173–184, Barcelona, Spain (Online). International Committee on Computational Linguistics.