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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Jin Zhang & Cong Wang & Guoqing Chen, 2021. "A Review Selection Method for Finding an Informative Subset from Online Reviews," INFORMS Journal on Computing, INFORMS, vol. 33(1), pages 280-299, January.
    • Handle: RePEc:inm:orijoc:v:33:y:2021:i:1:p:280-299
    DOI: 10.1287/ijoc.2019.0950
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    References listed on IDEAS

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    1. Pavel Krömer & Jan Platoš & Jana Nowaková & Václav Snášel, 2018. "Optimal column subset selection for image classification by genetic algorithms," Annals of Operations Research, Springer, vol. 265(2), pages 205-222, June.
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    3. Zunqiang Zhang & Guoqing Chen & Jin Zhang & Xunhua Guo & Qiang Wei, 2016. "Providing Consistent Opinions from Online Reviews: A Heuristic Stepwise Optimization Approach," INFORMS Journal on Computing, INFORMS, vol. 28(2), pages 236-250, May.
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    Cited by:

    1. Xiao-Jun Wang & Tao Liu & Weiguo Fan, 2023. "TGVx: Dynamic Personalized POI Deep Recommendation Model," INFORMS Journal on Computing, INFORMS, vol. 35(4), pages 786-796, July.
    2. Janina Seutter & Kristin Kutzner & Maren Stadtländer & Dennis Kundisch & Ralf Knackstedt, 2023. "“Sorry, too much information”—Designing online review systems that support information search and processing," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-19, December.
    3. Zhang, Chenxi & Xu, Zeshui, 2024. "Gaining insights for service improvement through unstructured text from online reviews," Journal of Retailing and Consumer Services, Elsevier, vol. 80(C).

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