A Review Selection Method for Finding an Informative Subset from Online Reviews

Jin Zhang (), Cong Wang () and Guoqing Chen ()
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Jin Zhang: School of Business, Renmin University of China, 100872 Beijing, China
Cong Wang: Guanghua School of Management, Peking University, 100871 Beijing, China, School of Economics and Management, Tsinghua University, 100084 Beijing, China; School of Economics and Management, Tsinghua University, 100084 Beijing, China
Guoqing Chen: School of Economics and Management, Tsinghua University, 100084 Beijing, China

INFORMS Journal on Computing, 2021, vol. 33, issue 1, 280-299

Abstract: Concerning the information overload of online reviews, this paper models a new review selection problem called the Informative Review Subset Selection problem (namely, IRSS) and demonstrates that it is NP-hard to solve and approximate. Furthermore, a novel heuristic method (namely, Combined Search-ComS) is proposed for seeking the solution to the problem and selecting a subset of reviews, which is consistent with the original review corpus in light of mutual information entropy. The proposed method is then comprehensively examined via extensive data experiments and a user study on Amazon data. Experimental results reveal the overall superiority of the proposed method in comparison with other extant methods of concern, showing that it is an effective way to select an informative subset of online reviews. The proposed method is deemed desirable and useful for online consumers and service providers.

Keywords: review selection; distribution divergence; heuristic method; feature coverage; opinion distribution (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:33:y:2021:i:1:p:280-299

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