A Topic Modeling Approach for Exploring Attraction of Dark Souls Series Reviews on Steam
Authors: Yang Yu, Ba Hung Nguyen, Duy Tai Dinh, Fangyu Yu, Tsutomu Fujinami, Van Nam Huynh
Abstract: Millions of players are active on gaming service platforms such as Twitch and Steam every month, but few researchers pay attention to their comments and experience. Among many types of games, hardcore games attract game enthusiasts with highly great difficulty. Hardcore games require players to invest much time to learn, so they have the highest engagement in the game genre. The Dark Souls series has always been a landmark masterpiece in hardcore games. It attracts many hardcore players with its challenging, subversive narrative, rich gameplay, and builds. The analysis of the success of the Dark Souls series is meaningful and inspiring for game developers. Many studies have analyzed the impact of Dark Souls games on player behaviors from a psychological and cultural perspective in current Game Research. However, few studies have investigated the attraction of the Dark Souls series from the players' perspective. Therefore, this research uses a topic model on massive review data of the Dark Soul series on Steam to reveal players' concerns. It aims to explore the charm of the Dark Souls series. The Dark Souls trilogy of three games produced by From Software and are available on Steam. Steam is the most significant worldwide digital distribution platform that offers comprehensive service, including installing and automatically updating games and community features such as friends lists and groups, cloud storage, and in-game voice and chat functionality. Since both Dark Souls (DS1) and Dark Souls 3 (DS3) was highly rated in this franchise, this study focuses on the masterpiece of the trilogy: Dark Souls. In the experiment, we collected a new review dataset of the Dark Souls series from Steam, including approximately 100,000 DS3 and 23,000 DS1 reviews up to October 2021. Then we used the Latent Dirichlet Allocation (LDA) model to categorize reviews from both datasets. Consequently, we uncovered 15 and 14 topics from DS1 and DS3, respectively. Among them, we used 13 topics that appear in both games to find out topics with high frequencies and positive ratings. The results indicate that most game players are concerned with five common topics: "experience", "combat", "character", "item", and "difficulty”, which are related to in-game items and boss fight design. Specifically, these topics appear in both games with a frequency and positive rating of over 10%. We also found stratification on "device" significantly lower than other topics, reflecting the developer's less optimizing game control when DS series were ported. Generally, the analysis results from this research provide high interpretability that can further support other studies in this field.
Keywords: Dark Souls 3, Online Reviews, Topic Modeling, Steam
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