Explaining and Predicting Helpfulness and Funniness of Online Reviews on the Steam Platform

Zhi Wang, Victor Chang, Gergely Horvath

Source Title: Journal of Global Information Management (JGIM)29(6)

ISSN: 1062-7375|EISSN: 1533-7995|EISBN13: 9781799872627|DOI: 10.4018/JGIM.20211101.oa16

MLA

Wang, Zhi, et al. "Explaining and Predicting Helpfulness and Funniness of Online Reviews on the Steam Platform." JGIM vol.29, no.6 2021: pp.1-23. http://doi.org/10.4018/JGIM.20211101.oa16

APA

Wang, Z., Chang, V., & Horvath, G. (2021). Explaining and Predicting Helpfulness and Funniness of Online Reviews on the Steam Platform. Journal of Global Information Management (JGIM), 29(6), 1-23. http://doi.org/10.4018/JGIM.20211101.oa16

Chicago

Wang, Zhi, Victor Chang, and Gergely Horvath. "Explaining and Predicting Helpfulness and Funniness of Online Reviews on the Steam Platform," Journal of Global Information Management (JGIM) 29, no.6: 1-23. http://doi.org/10.4018/JGIM.20211101.oa16

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Abstract

Online review is a crucial display content of many online shopping platforms and an essential source of product information for consumers. Low-quality reviews often cause inconvenience to the platform and review readers. This article aims to help Steam, one of the largest digital distribution platforms, predict the review helpfulness and funniness. Via Python, 480,000 game reviews related data for 20 games were captured for analysis. This article analyzed the impact of three categories of influencing factors on the usefulness and funniness of game reviews, which are characteristics of review, reviewer and game. Additionally, by using the Random Forest-based classifier, the usefulness of reviews could be accurately predicted, while for funniness, Gradient Boosting Decision Tree was the better choice. This article applied research on the usefulness of reviews to game products and proposed research on the funniness of reviews.