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Deriving topic-related and interaction features to predict top attractive reviews for a specific business entity

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  • Eunjung Lee
  • Huimin Zhao

Abstract

As large volumes of online reviews are being generated, both online businesses and customers are confronted with big data challenges. Previous studies have developed various methods to predict the helpfulness of online reviews. These methods have disregarded the aspects of the business entities when dealing with datasets for prediction and evaluation and have not considered interactions between a review and the target business entity. In this paper, we propose a novel method to predict the top attractive reviews for a specific business entity. We also propose topic-related features to characterise the topics in a review and interaction features to reflect relationships between a review and the business entity it covers. Our empirical evaluation shows the utility of our proposed method and features.

Suggested Citation

  • Eunjung Lee & Huimin Zhao, 2020. "Deriving topic-related and interaction features to predict top attractive reviews for a specific business entity," Journal of Business Analytics, Taylor & Francis Journals, vol. 3(1), pages 17-31, January.
    • Handle: RePEc:taf:tjbaxx:v:3:y:2020:i:1:p:17-31
    DOI: 10.1080/2573234X.2020.1768808
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