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

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