Predicting The Helpfulness Of Online Product Reviewers: A Data Mining Approach

Authors

Han-Wei Hsiao, Department of Information Management, National University of Kaohsiung, Kaohsiung, Taiwan, R.O.CFollow
Chih-Ping Wei, Department of Information Management, National Taiwan University, Taipei, Taiwan, R.O.CFollow
Yi-Cheng Ku, Department of Computer Science and Information Management, Providence University, Taichung, Taiwan, R.O.CFollow
Luisa Angelica Chen Ng, Institute of Technology Management, National Tsing Hua University, Hsinchu, Taiwan, R.O.C

Abstract

The purpose of this study is to propose a data mining approach to predict the helpfulness scores of online product reviewers. Such prediction can facilitate consumers to judge whether to believe or disbelieve reviews written by different reviewers and can help e-stores or third-party product review websites to target and retain quality reviewers. In this study, we identify eight independent variables from the perspectives of reviewers’ review behavior and trust network to predict the helpfulness scores for these reviewers. We adopt M5 and SVM Regression as our underlying learning algorithms. Our empirical evaluation results on the basis of two product categories (i.e., Car and Computer) suggest that our proposed helpfulness prediction technique can predict the helpfulness scores of online product reviewers.

Recommended Citation

Hsiao, Han-Wei; Wei, Chih-Ping; Ku, Yi-Cheng; and Angelica Chen Ng, Luisa, "Predicting The Helpfulness Of Online Product Reviewers: A Data Mining Approach" (2012). PACIS 2012 Proceedings. 134.
https://aisel.aisnet.org/pacis2012/134