Unraveling Player's Insights: A Comparative Analysis of Topic Modeling Techniques on Game Reviews and Video Game Developers' Perspectives

Abstract

Game reviews function as an important customer-created resource for game studies as they allow practitioners and developers to analyze players' opinions. Despite this, there are few studies that undertake comparative evaluations of topic modeling approaches in the context of video game data analysis or assess the results' practical efficacy. Accordingly, this article aims to evaluate the performance of three topic modeling algorithms—latent Dirichlet allocation, non-negative matrix factorization, and BERTopic—as utilized within game reviews study and further to examine the results' reception within the video game industry. This study first uses the game No Man's Sky as a case study to evaluate the performance of different models in the same game context. According to our experiments based on Steam game reviews, the topic's Uci coherence score as identified by the BERTopic model can reach 0.279, which is higher than the other two models, with the extracted keywords allowing humans to interpret the themes when mapping them to original reviews. Semi-structured interviews with seven developers are then presented, which demonstrate that the information we provided is useful to improve their games and track players' opinions.