Analyzing Gamer Complaints in Reviews of Cross-Platform Video Games on Steam

Video gaming now represents the largest category in the entertainment industry in terms of revenue. To expand their market share, game developers are creating more cross-platform games, which are compatible with various platforms, including PCs, consoles, and smartphones. However, creating such games poses challenges as developers encounter platform-specific issues that may only surface on one of the target platforms. Consequently, many ported games fail due to careless adaptation from one exclusive platform to another. This paper presents the first empirical study on cross-platform issues by analyzing game users’ reviews for video games on both PC and game console(s). Our findings reveal that platform-related issues occur more frequently on the PC side, particularly for games that are ported from consoles. To address this challenge, we develop machine learning-based approaches to automatically identify and categorize reviews discussing platform-related issues, achieving a reasonable classification performance with 79.73% to 90.06% accuracy. Our approach would help cross-platform game developers save considerable time when analyzing user reviews.