Predicting the Helpfulness of Online Product Reviews: A Multilingual Approach

Electronic Commerce Research and Applications 27(1), 2018, pp. 1-10. https://doi.org/10.1016/j.elerap.2017.10.008

The University of Auckland Business School Research Paper Series

Posted: 18 Oct 2021

See all articles by Ying Zhang

Ying Zhang

University of Auckland Business School

Zhijie Lin

Tsinghua University - School of Economics and Management

Date Written: 2018

Abstract

Identifying helpful reviews from massive review data has been a hot topic in the past decade. While existing research on review helpfulness estimation and prediction is primarily sourced from English reviews, non-English reviews may also provide useful consumer opinion information and should not be neglected. In this study, we propose a review helpfulness prediction framework that processes and uses multilingual sources of reviews to generate relevant business insights. Adopting a design science research approach, we design, implement, evaluate and deliver an IT artifact (i.e., our framework) that predicts the helpfulness of a review and accounts for non-English reviews. Our evaluations suggest that we achieve better performance on review helpfulness prediction and classification by including the variables generated by our instantiated multilingual system. By demonstrating the feasibility of our proposed framework for multilingual business intelligence applications, we contribute to the literature on business intelligence and provide important practical implications to practitioners.

Keywords: Word-of-mouth, Product reviews, Multilingual reviews, Review helpfulness, Prediction

Suggested Citation

Zhang, Ying and Lin, Zhijie, Predicting the Helpfulness of Online Product Reviews: A Multilingual Approach (2018). Electronic Commerce Research and Applications 27(1), 2018, pp. 1-10. https://doi.org/10.1016/j.elerap.2017.10.008 , The University of Auckland Business School Research Paper Series, Available at SSRN: https://ssrn.com/abstract=3942168

Ying Zhang (Contact Author)

University of Auckland Business School ( email )

12 Grafton Rd
Private Bag 92019
Auckland, 1010
New Zealand

HOME PAGE: https://unidirectory.auckland.ac.nz/people/profile/zhang-ying

Zhijie Lin

Tsinghua University - School of Economics and Management ( email )

Haidian District
Beijing, Beijing 100084
China

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