Explaining and Predicting Helpfulness and Funniness of Online Reviews on the Steam Platform
The online review is a crucial display 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.
Duke Scholars
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- Information Systems
- 4609 Information systems
- 3503 Business systems in context
- 1702 Cognitive Sciences
- 0807 Library and Information Studies
- 0806 Information Systems
Citation
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Related Subject Headings
- Information Systems
- 4609 Information systems
- 3503 Business systems in context
- 1702 Cognitive Sciences
- 0807 Library and Information Studies
- 0806 Information Systems