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Predicting Online Reviewer Popularity: A Comparative Analysis of Machine Learning Techniques

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Internetworked World (WEB 2016)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 296))

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Abstract

Online customer reviews have been found to vary in their level of influence on customers’ purchase decisions depending on both review and reviewer characteristics. It is logical to expect reviews written by popular reviewers to wield more influence over customers, and therefore an investigation into factors which can help explain and predict reviewer popularity should have high academic and practical implications. We made a novel attempt at using machine learning techniques to classify reviewers into high/low popularity based on their profile characteristics. We compared five different models, and found the neural network model to be the best in terms of overall accuracy (84.2%). Total helpfulness votes received by a reviewer was the top determinant of popularity. Based on this work, businesses can identify potentially influential reviewers to request them for reviews. This research-in-progress can be extended using more factors and models to further enhance the accuracy rate.

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Notes

  1. 1.

    http://marketingland.com/survey-customers-more-frustrated-by-how-long-it-takes-to-resolve-a-customer-service-issue-than-the-resolution-38756

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Authors and Affiliations

  1. Management Information Systems, Indian Institute of Management Calcutta, Calcutta, India

    Samadrita Bhattacharyya, Shankhadeep Banerjee & Indranil Bose

Authors
  1. Samadrita Bhattacharyya
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  2. Shankhadeep Banerjee
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  3. Indranil Bose
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Corresponding author

Correspondence to Samadrita Bhattacharyya .

Editor information

Editors and Affiliations

  1. University of Washington, Seattle, Washington, USA

    Ming Fan

  2. University of Turku, Turku, Finland

    Jukka Heikkilä

  3. University of Turku, Turku, Finland

    Hongxiu Li

  4. University of Illinois, Urbana-Champaign, Illinois, USA

    Michael J. Shaw

  5. Georgia Institute of Technology, Atlanta, Georgia, USA

    Han Zhang

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Bhattacharyya, S., Banerjee, S., Bose, I. (2017). Predicting Online Reviewer Popularity: A Comparative Analysis of Machine Learning Techniques. In: Fan, M., Heikkilä, J., Li, H., Shaw, M., Zhang, H. (eds) Internetworked World. WEB 2016. Lecture Notes in Business Information Processing, vol 296. Springer, Cham. https://doi.org/10.1007/978-3-319-69644-7_3

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  • DOI: https://doi.org/10.1007/978-3-319-69644-7_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69643-0

  • Online ISBN: 978-3-319-69644-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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