Analyzing VR Game User Experience by Genre: A Text-Mining Approach on Meta Quest Store Reviews

Abstract

With the rapid expansion of the virtual reality (VR) market, user interest in VR games has increased significantly. However, empirical research on the user experience in VR games remains relatively underdeveloped. Despite the growing popularity and commercial success of VR gaming, there is a lack of comprehensive studies analyzing the impact of different aspects of VR games on user satisfaction and engagement. This gap includes insufficient research on the categorization of VR game genres, the identification of user challenges, and variations in user experiences across these genres. Our study aims to fill this gap by analyzing data from the Meta Quest store using K-means clustering and LDA (Latent Dirichlet Allocation) to categorize the representative genres of VR games. By employing text-mining techniques to conduct a detailed analysis of user experience, we effectively elucidate the primary issues and nuanced differences in user responses across various genres. Our findings serve as a valuable reference for researchers aiming to design games that align with VR user expectations. Furthermore, our study provides a foundational dataset for researchers aiming to enhance the user experience in VR games and suggests ways to increase the immersion and enjoyment of VR gameplay.