IDEAS home Printed from https://ideas.repec.org/a/inm/orijoc/v33y2021i1p280-299.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/ijoc.2019.0950
    Download Restriction: no
    File URL: https://libkey.io/10.1287/ijoc.2019.0950?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    2. Rohit Aggarwal & Ram Gopal & Ramesh Sankaranarayanan & Param Vir Singh, 2012. "Blog, Blogger, and the Firm: Can Negative Employee Posts Lead to Positive Outcomes?," Information Systems Research, INFORMS, vol. 23(2), pages 306-322, June.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dominik Gutt & Jürgen Neumann & Steffen Zimmermann & Dennis Kundisch & Jianqing Chen, 2018. "Design of Review Systems - A Strategic Instrument to shape Online Review Behavior and Economic Outcomes," Working Papers Dissertations 42, Paderborn University, Faculty of Business Administration and Economics.
    2. Chung, Alexander Q.H. & Andreev, Pavel & Benyoucef, Morad & Duane, Aidan & O’Reilly, Philip, 2017. "Managing an organisation’s social media presence: An empirical stages of growth model," International Journal of Information Management, Elsevier, vol. 37(1), pages 1405-1417.
    3. Param Vir Singh & Nachiketa Sahoo & Tridas Mukhopadhyay, 2014. "How to Attract and Retain Readers in Enterprise Blogging?," Information Systems Research, INFORMS, vol. 25(1), pages 35-52, March.
    4. Agarwal, Sumit & Wang, Long & Yang, Yang, 2021. "Impact of transboundary air pollution on service quality and consumer satisfaction," Journal of Economic Behavior & Organization, Elsevier, vol. 192(C), pages 357-380.
    5. Chung, Alexander Q.H. & Andreev, Pavel & Benyoucef, Morad & Duane, Aidan & O’Reilly, Philip, 2018. "Where the shoe pinches: Realizing dominant problems as an organizational social media business profile evolves," International Journal of Information Management, Elsevier, vol. 41(C), pages 33-49.
    6. He, Wu & Zha, Shenghua & Li, Ling, 2013. "Social media competitive analysis and text mining: A case study in the pizza industry," International Journal of Information Management, Elsevier, vol. 33(3), pages 464-472.
    7. Guo, Mengzhuo & Liao, Xiuwu & Liu, Jiapeng & Zhang, Qingpeng, 2020. "Consumer preference analysis: A data-driven multiple criteria approach integrating online information," Omega, Elsevier, vol. 96(C).
    8. Seshadri Tirunillai & Gerard J. Tellis, 2017. "Does Offline TV Advertising Affect Online Chatter? Quasi-Experimental Analysis Using Synthetic Control," Marketing Science, INFORMS, vol. 36(6), pages 862-878, November.
    9. Hyelim Oh & Khim-Yong Goh & Tuan Q. Phan, 2023. "Are You What You Tweet? The Impact of Sentiment on Digital News Consumption and Social Media Sharing," Information Systems Research, INFORMS, vol. 34(1), pages 111-136, March.
    10. Chae, Bongsug (Kevin) & McHaney, Roger & Sheu, Chwen, 2020. "Exploring social media use in B2B supply chain operations," Business Horizons, Elsevier, vol. 63(1), pages 73-84.
    11. Kawaljeet Kaur Kapoor & Kuttimani Tamilmani & Nripendra P. Rana & Pushp Patil & Yogesh K. Dwivedi & Sridhar Nerur, 2018. "Advances in Social Media Research: Past, Present and Future," Information Systems Frontiers, Springer, vol. 20(3), pages 531-558, June.
    12. Yan Huang & Param Vir Singh & Anindya Ghose, 2015. "A Structural Model of Employee Behavioral Dynamics in Enterprise Social Media," Management Science, INFORMS, vol. 61(12), pages 2825-2844, December.
    13. Zhang, Min & Sun, Lin & Wang, G. Alan & Li, Yuzhuo & He, Shuguang, 2022. "Using neutral sentiment reviews to improve customer requirement identification and product design strategies," International Journal of Production Economics, Elsevier, vol. 254(C).
    14. Liu, Fan & Liao, Huchang & Al-Barakati, Abdullah, 2023. "Physician selection based on user-generated content considering interactive criteria and risk preferences of patients," Omega, Elsevier, vol. 115(C).
    15. Jindong Qin & Pan Zheng & Xiaojun Wang, 2024. "Product Redesign and Innovation Based on Online Reviews: A Multistage Combined Search Method," INFORMS Journal on Computing, INFORMS, vol. 36(3), pages 742-765, May.
    16. Stephen Nabareseh & Eric Afful-Dadzie & Petr Klimek, 2018. "Leveraging Fine-Grained Sentiment Analysis for Competitivity," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 17(02), pages 1-20, June.
    17. Jessica Braojos & Jose Benitez & Javier Llorens, 2019. "How do social commerce-IT capabilities influence firm performance? Theory and empirical evidence," Post-Print hal-02057780, HAL.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:orijoc:v:33:y:2021:i:1:p:280-299. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.