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Exploring thematic composition of online reviews: A topic modeling approach

Author

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  • Vamsi Vallurupalli

    (Indian Institute of Management Calcutta)

  • Indranil Bose

    (Indian Institute of Management Calcutta)

Abstract

Online reviews are a critical component of the retail business ecosystem today. They help consumers share feedback and readers make informed choices. As such, it is important to understand the mechanism driving the creation of reviews and identify factors which make them useful for readers. Extant work in this field has largely ignored the distribution of thematic content in reviews and its role in review diagnosticity. This article attempts to bridge the gap. A novel approach is proposed to explore the distribution of thematic content in reviews, in terms of underlying topics, and test its impact on influence of reviews. The approach is illustrated through a case study using data from Yelp. Implications of the study for theory and practice are discussed.

Suggested Citation

  • Vamsi Vallurupalli & Indranil Bose, 2020. "Exploring thematic composition of online reviews: A topic modeling approach," Electronic Markets, Springer;IIM University of St. Gallen, vol. 30(4), pages 791-804, December.
    • Handle: RePEc:spr:elmark:v:30:y:2020:i:4:d:10.1007_s12525-020-00397-5
    DOI: 10.1007/s12525-020-00397-5
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    References listed on IDEAS

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    Cited by:

    1. Andreas J. Steur & Fabian Fritzsche & Mischa Seiter, 2022. "It’s all about the text: An experimental investigation of inconsistent reviews on restaurant booking platforms," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1187-1220, September.
    2. Hoon S. Choi & Michele Maasberg, 2022. "An empirical analysis of experienced reviewers in online communities: what, how, and why to review," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1293-1310, September.
    3. Weng Marc Lim & Gaurav Gupta & Baidyanath Biswas & Rohit Gupta, 2022. "Collaborative consumption continuance: a mixed-methods analysis of the service quality-loyalty relationship in ride-sharing services," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1463-1484, September.
    4. Tim Kollmer & Andreas Eckhardt & Victoria Reibenspiess, 2022. "Explaining consumer suspicion: insights of a vignette study on online product reviews," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1221-1238, September.

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    More about this item

    Keywords

    Latent Dirichlet allocation; Online reviews; Review influence; Thematic content; Topic modeling; Yelp;
    All these keywords.

    JEL classification:

    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

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