Open AccessExplaining and Predicting Helpfulness & Funniness of Online Reviews on the Steam Platform
Publication year - 2021
Publication title -
journal of global information management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.315
H-Index - 41
eISSN - 1533-7995
pISSN - 1062-7375
DOI - 10.4018/jgim.20211101oa17
Subject(s) - helpfulness , computer science , boosting (machine learning) , python (programming language) , artificial intelligence , machine learning , multimedia , psychology , social psychology , operating system
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.