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Predicting the Helpfulness of Online Customer Reviews across Different Product Types

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  • Yoon-Joo Park

    (Department of Business Administration, Seoul National University of Science and Technology, Seoul 01811, Korea)

Abstract

Online customer reviews are a sustainable form of word of mouth (WOM) which play an increasingly important role in e-commerce. However, low quality reviews can often cause inconvenience to review readers. The purpose of this paper is to automatically predict the helpfulness of reviews. This paper analyzes the characteristics embedded in product reviews across five different product types and explores their effects on review helpfulness. Furthermore, four data mining methods were examined to determine the one that best predicts review helpfulness for each product type using five real-life review datasets obtained from Amazon.com. The results show that reviews for different product types have different psychological and linguistic characteristics and the factors affecting the review helpfulness of them are also different. Our findings also indicate that the support vector regression method predicts review helpfulness most accurately among the four methods for all five datasets. This study contributes to improving efficient utilization of online reviews.

Suggested Citation

  • Yoon-Joo Park, 2018. "Predicting the Helpfulness of Online Customer Reviews across Different Product Types," Sustainability, MDPI, vol. 10(6), pages 1-20, May.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:6:p:1735-:d:149015
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    References listed on IDEAS

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    1. Laura Auria & Rouslan A. Moro, 2008. "Support Vector Machines (SVM) as a Technique for Solvency Analysis," Discussion Papers of DIW Berlin 811, DIW Berlin, German Institute for Economic Research.
    2. Chris Forman & Anindya Ghose & Batia Wiesenfeld, 2008. "Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets," Information Systems Research, INFORMS, vol. 19(3), pages 291-313, September.
    3. Pan, Yue & Zhang, Jason Q., 2011. "Born Unequal: A Study of the Helpfulness of User-Generated Product Reviews," Journal of Retailing, Elsevier, vol. 87(4), pages 598-612.
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    Cited by:

    1. Yen-Liang Chen & Chia-Ling Chang & An-Qiao Sung, 2021. "Predicting eWOM’s Influence on Purchase Intention Based on Helpfulness, Credibility, Information Quality and Professionalism," Sustainability, MDPI, vol. 13(13), pages 1-19, July.
    2. Chunting Liu & Shanshan Wang & Guozhu Jia, 2020. "Exploring E-Commerce Big Data and Customer-Perceived Value: An Empirical Study on Chinese Online Customers," Sustainability, MDPI, vol. 12(20), pages 1-22, October.
    3. Nazir, Sajjad & Khadim, Sahar & Ali Asadullah, Muhammad & Syed, Nausheen, 2023. "Exploring the influence of artificial intelligence technology on consumer repurchase intention: The mediation and moderation approach," Technology in Society, Elsevier, vol. 72(C).
    4. Raoofpanah, Iman & Zamudio, César & Groening, Christopher, 2023. "Review reader segmentation based on the heterogeneous impacts of review and reviewer attributes on review helpfulness: A study involving ZIP code data," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    5. Alejandro García-Jurado & José Javier Pérez-Barea & Francisco Fernández-Navarro, 2021. "Towards Digital Sustainability: Profiles of Millennial Reviewers, Reputation Scores and Intrinsic Motivation Matter," Sustainability, MDPI, vol. 13(6), pages 1-19, March.
    6. Moradi, Masoud & Dass, Mayukh & Kumar, Piyush, 2023. "Differential effects of analytical versus emotional rhetorical style on review helpfulness," Journal of Business Research, Elsevier, vol. 154(C).
    7. Yi Luo & Xiaowei Xu, 2019. "Predicting the Helpfulness of Online Restaurant Reviews Using Different Machine Learning Algorithms: A Case Study of Yelp," Sustainability, MDPI, vol. 11(19), pages 1-17, September.

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