Analyzing VR Game User Experience by Genre
VR 게임 장르별 사용자 경험 분석
- 주제(키워드) VR games , game design , meta quest games , text mining , user experience , Latent Dirichlet Allocation (LDA)
- 주제(DDC) 006.31
- 발행기관 아주대학교 일반대학원
- 지도교수 정태선
- 발행년도 2025
- 학위수여년월 2025. 8
- 학위명 석사
- 학과 및 전공 일반대학원 인공지능학과
- 실제URI http://www.dcollection.net/handler/ajou/000000035036
- 본문언어 한국어
- 저작권 아주대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
As immersive technologies continue to advance, the virtual reality (VR) gaming industry has experienced remarkable growth, attracting a rapidly expanding global user base. This widespread adoption underscores the increasing need for a deeper investigation into user experience (UX) within VR gaming environments. Despite the commercial success of VR games, academic research on user-centered factors such as satisfaction, engagement, and interaction behavior—remains limited. Existing studies often focus on popular titles or isolated gameplay mechanics, resulting in an incomplete understanding of how user experiences vary across game genres. A key challenge in this area is the lack of standardized genre classifications on platforms like the Meta Quest Store, which complicates efforts to analyze user feedback at scale. To address this issue, our study leverages a large-scale dataset comprising over 100,000 user reviews from the Meta Quest platform. We apply K-means clustering to reorganize VR game genres and employ Latent Dirichlet Allocation (LDA) to extract dominant themes and user concerns within each category. Our findings reveal genre-specific UX challenges, for example, motion sickness in shooter games, hand-tracking issues in rhythm games, and spatial constraints in horror titles. These insights contribute to a clearer understanding of user expectations and inter-genre dynamics, offering valuable guidance for future game development. Based on the results, we propose a genre-level UX taxonomy and present target design recommendations aimed at enhancing immersion, usability, and overall player satisfaction. Ultimately, this research introduces a scalable, data-driven framework for evaluating user experience in VR games and lays a practical foundation for designing more engaging, genre-specific immersive environments.
more목차
1 Introduction 1
1.1 VR Market Growth 1
1.2 Limitations in VR User Experience 2
1.3 Genre Classification 3
1.4 Research Questions 3
2 Research Method 6
2.1 Overview 6
2.2 Data Processing 8
2.2.1 Data Collection 8
2.2.2 Normalization 8
2.2.3 Word Embedding 9
2.3 Data Analysis 10
2.3.1 K-means Clustering 11
2.3.2 Topic Modeling 14
3 LDA Analysis 18
3.1 Shooting 21
3.2 Music 21
3.3 Strategy 22
3.4 Horror 22
3.5 Action 23
3.6 Sports 23
3.7 Puzzle 23
4 Discussion 25
4.1 Experience Characteristics 25
4.2 Genre Correlation 27
4.2.1 Similarity Computation 27
4.2.2 Interpretation 27
4.2.3 Key Observations 28
5 Conclusion 30
5.1 Key Contribution 30
5.2 Limitations 30
5.3 Future Research Direction 31
References 32
Appendix A: LDA Output 34
Appendix B: Word Cloud 37
A List of Publications 39
