Research on customer opinion summarization using topic mining and deep neural network

Ming Hong and Heyong Wang

Mathematics and Computers in Simulation (MATCOM), 2021, vol. 185, issue C, 88-114

Abstract: Product reviews are of great commercial value for online shopping market. The identification of customer opinions from product reviews is helpful to improve the marketing decisions of customers, sellers and producers. This paper proposes a novel framework for summarizing customer opinions from product reviews. Firstly, our framework identifies grammatically and semantically meaningful phrases which contain product attributes and their corresponding opinions from original product reviews by using grammar rules and the latent Dirichlet allocation (LDA) model. Secondly, our framework generates readable and simple summaries from the identified phrases automatically by using the deep neural network. The summaries provide users the valuable opinions on product attributes. Moreover, our framework provides an interactive mode for users to choose product attributes which they are interested for generate personalized summaries to help users focus on the most concerned opinions. Experimental results on six datasets demonstrate effectiveness of our framework.

Keywords: Product reviews; Topic mining; Deep neural network (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:185:y:2021:i:c:p:88-114

DOI: 10.1016/j.matcom.2020.12.009

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