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

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Wang, Z; Chang, V; Horvath, G

Published Date

  • November 2021

Published In

Volume / Issue

  • 29 / 6

Start / End Page

  • 1 - 23

Published By

Electronic International Standard Serial Number (EISSN)

  • 1533-7995

International Standard Serial Number (ISSN)

  • 1062-7375

Digital Object Identifier (DOI)

  • 10.4018/jgim.20211101.oa16

Language

  • en