Evaluation of Mobile Game Apps Emotional Side: A Sentiment Analysis on Mobile Game App Reviews

Öz

Mobile platforms have become one of the important components of digital marketing due to their proliferation in recent years and their place in the lives of today's consumers. Mobile game applications, which have a promising scope in the mobile field, represent one of the important areas with various components such as game categories, game elements and game experience. This study focuses on the game categories component and extends the current literature, which examines game categories in limited contexts. The study aims to evaluate the sentiment side of user reviews, examines 17 mobile game categories and focuses on the context of Google Play Store mobile app reviews. 83.118 reviews from 90 mobile game apps in 17 categories are used as the study sample and the study employs sentiment analysis methodology through a transformers-based model named "Emotion English DistilRoBERTa-base". Evaluation of reviews is presented in three stages: rating score distribution, sentiment category distribution among game category types and individual sentiment category evaluation. In the first stage of the study findings, user evaluations are grouped according to their score levels. Accordingly, a dichotomy assessment is made over a distribution concentrated on 1 star and 5 stars. In the second stage of the study, percentage distributions of emotion categories are given over game categories and joy and neutral emotion categories are concluded as the most common emotion categories. Fear sentiment category has the lowest percentage among the categories and anger, disgust and sadness sentiment categories have varying proportions among the categories. Finally, emotion categories are analyzed individually based on the game categories in which they are most frequently found. The study contributes theoretically to electronic word-of-mouth marketing theory and game research and from a sectoral perspective, it helps industrial practices from an emotion perspective through the comprehensive approach used in the study.

Anahtar Kelimeler

• Game Consumption
• Mobile Apps
• Gaming
• Online Review
• Mobile Consumption

Mobil Oyun Uygulamalarının Duygusal Yönünün Değerlendirilmesi: Mobil Oyun Uygulaması İncelemeleri Üzerine Bir Duygu Analizi

Öz

Mobil platformların son yıllardaki yaygınlaşması, günümüz tüketicisi açısından ifade ettiği anlam itibariyle dijital pazarlamanın önemli bileşenlerinden biri haline gelmiştir. Mobil alanda gelecek vaat eden bir kapsamda yer alan mobil oyun uygulamaları; oyun kategorileri, oyun öğeleri ve oyun deneyimi gibi çeşitli bileşenlere sahip önemli alanlardan birini ifade etmektedir. Bu çalışma, oyun kategorileri bileşenine odaklanmakta ve oyun kategorilerini sınırlı bağlamlarda inceleyen mevcut literatürü genişletmektedir. Çalışma, kullanıcı değerlendirmelerinin duygu yönünü değerlendirmeyi amaçlamakta olup, 17 mobil oyun kategorisini inceler ve Google Play Store mobil uygulama değerlendirmeleri bağlamına odaklanmaktadır. 17 kategorideki 90 mobil oyun uygulamasından 83.118 inceleme çalışma örneklemi olarak kullanılmış ve çalışmada "Emotion English DistilRoBERTa-base" adlı transformatörlere dayalı bir model aracılığıyla duygu analizi metodolojisi uygulanmıştır. Kullanıcı değerlendirmelerinin ele alınması; derecelendirme puanı dağılımı, duygu kategorilerinin oyun kategorisi türleri arasında dağılımı ve duygu kategorilerinin tekli olarak incelenmesi olmak üzere üç aşamada sunulmuştur. Çalışma bulgularının ilk aşamasında kullanıcı değerlendirmeleri puan düzeylerine göre gruplanmıştır. Buna göre 1 yıldız ve 5 yıldızda yoğunlaşan bir dağılım üzerinden ikilik değerlendirmesi yapılmıştır. Çalışmanın ikinci aşamasında ise oyun kategorileri üzerinden duygu kategorilerinin yüzdelik olarak dağılımlarına yer verilmiş, neşe ve nötr duygu kategorileri en yaygın olan duygu kategorileri olarak bulunmuştur. Korku duygusu kategorisi kategoriler arasında en düşük yüzdeye sahiptir ve öfke, iğrenme ve üzüntü duygu kategorileri, oyun kategorileri arasında değişen oranlara sahiptir. Son olarak, duygu kategorileri en sık bulundukları oyun kategorilerine göre ayrı ayrı analiz edilmiştir. Çalışma teorik açıdan elektronik kulaktan kulağa pazarlama kuramına ve oyun araştırmalarına katkı sağlamakta olup, sektörel açıdan ise çalışmada kullanılan kapsamlı yaklaşım üzerinden sektörel uygulamalara duygu perspektifinden yardımcı olmaktadır.

Anahtar Kelimeler

• Mobil Uygulamalar
• Oyun
• Çevrimiçi Değerlendirme
• Mobil Tüketim
• Oyun Tüketimi

Kaynakça

1. Allsop, D. T., Bassett, B. R. and Hoskins, J. A. (2007). Word-of-Mouth Research: Principles And Applications. Journal of Advertising Research, 47(4), 398-411.
2. Bengio, Y., Ducharme, R., Vincent, P., & Jauvin, C. (2003). A neural probabilistic language model. Journal of machine learning research, 3(Feb), 1137-1155.
3. Chaudhry, B. M., Islam, M. U. and Chawla, N. V. (2024). Longitudinal Evaluation of Casual Puzzle Tablet Games by Older Adults, Proceedings of the 2024 ACM Designing Interactive Systems Conference, 1-5 July 2024, IT University, Copenhagen-DK. 2073-2087.
4. Chesham, A., Wyss, P., Müri, R. M., Mosimann, U. P. and Nef, T. (2017). What Older People Like To Play: Genre Preferences and Acceptance of Casual Games. JMIR Serious Games, 5(2), 1-15.
5. Colombo, S., Hansson, P. and Nyström, M. B. (2023). Mining Players’ Experience in Computer Games: Immersion Affects Flow But Not Presence. Computers in Human Behavior Reports, 12, 1-10.
6. Csikszentmihalyi, M. (1990). Flow: The Psychology of Optimal Experience, Chicago: Harper Perennial.
7. Csikszentmihalyi, M. (2013). Flow: The Psychology of Happiness, Chicago: Harper Perennial.
8. D’astous, A. and Gagnon, K. (2007). An Inquiry into the Factors That Impact on Consumer Appreciation of a Board Game. Journal of Consumer Marketing, 24(2), 80-89.

9. Data.ai. (2024). State of Mobile 2024. Access: 27 June 2024, https://sensortower.com/state-of-mobile-2024
10. Devika, M. D., Sunitha, C. and Ganesh, A. (2016). Sentiment Analysis: A Comparative Study on Different Approaches. Procedia Computer Science, 87, 44-49.
11. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019, June). Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers) (pp. 4171-4186).
12. Entertainment Software Association. (2024). 2024 Essential Facts About the U.S. Video Game Industry. Access: 17 June 2024 https://www.theesa.com/wp-content/uploads/2024/05/Essential-Facts-2024-FINAL.pdf
13. Feijoo, C., Gómez-Barroso, J. L., Aguado, J. M. and Ramos, S. (2012). Mobile Gaming: Industry Challenges and Policy Implications. Telecommunications Policy, 36(3), 212-221.
14. Filieri, R., Raguseo, E. and Vitari, C. (2019). What Moderates The Influence of Extremely Negative Ratings? The Role of Review And Reviewer Characteristics. International Journal of Hospitality Management, 77, 333-341.
15. Guo, Y., Barnes, S. J. and Jia, Q. (2017). Mining Meaning From Online Ratings and Reviews: Tourist Satisfaction Analysis Using Latent Dirichlet Allocation. Tourism Management, 59, 467-483.
16. Hamari, J., Malik, A., Koski, J. and Johri, A. (2019). Uses and Gratifications of Pokémon Go: Why Do People Play Mobile Location-Based Augmented Reality Games?. International Journal of Human-Computer Interaction, 35(9), 804-819.
17. Hartmann, J. (2022). Emotion English DistilRoBERTa-base. Access: 16 June 2024 https://huggingface.co/j-hartmann/emotion-english-distilroberta-base/
18. Ho, S. C. and Tu, Y. C. (2012). The Investigation of Online Reviews of Mobile Games. E-Life: Web-Enabled Convergence of Commerce, Work, And Social Life, 10th Workshop on E-Business, 12 December 2015, Texas/USA, Berlin: Springer Heidelberg.130-139.
19. Jansz, J. and Tanis, M. (2007). Appeal of Playing Online First Person Shooter Games. Cyberpsychology and Behavior, 10(1), 133-136.
20. Katz, E., Blumler, J. G. and Gurevitch, M. (1973). Uses And Gratifications Research. The Public Opinion Quarterly, 37(4), 509-523.
21. Kleinginna Jr, P. R., & Kleinginna, A. M. (1981). A categorized list of emotion definitions, with suggestions for a consensual definition. Motivation and emotion, 5(4), 345-379.
22. Litvin, S. W., Goldsmith, R. E. and Pan, B. (2008). Electronic Word-of-Mouth in Hospitality And Tourism Management. Tourism Management, 29(3), 458-468.
23. Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., ... & Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692.
24. Liu, B. (2022). Sentiment analysis and opinion mining. Springer Nature.
25. Medhat, W., Hassan, A. and Korashy, H. (2014). Sentiment Analysis Algorithms And Applications: A Survey. Ain Shams Engineering Journal, 5(4), 1093-1113.
26. Merhi, M. I. (2016). Towards a Framework for Online Game Adoption. Computers in Human Behavior, 60, 253-263.
27. Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
28. Mingyu, J. (2024). Google Play Scraper. GitHub Repository. Access: 15 June 2024 https://github.com/JoMingyu/google-play-scraper
29. Nakamura, J. and Csikszentmihalyi, M. (2002). The Concept of Flow. Handbook of Positive Psychology, 89, 105.
30. Nam, K. and Kim, H. J. (2020). The Determinants of Mobile Game Success in South Korea. Telecommunications Policy, 44(2), 1-13.
31. Pyae, A., Mika, L. and Smed, J. (2019). Understanding Players’ Experiences in Location-based Augmented Reality Mobile Games: A Case of Pokémon Go. Extended Abstracts Publication of the Annual Symposium on Computer-Human Interaction in Play Conference 2019, 22-25 October 2019, 535-541.
32. Pew Research Center. (2018). 5 Facts about Americans and Video Games. Access: 27 June 2024 https://www.pewresearch.org/short-reads/2018/09/17/5-facts-about-americans-and-video-games/
33. Pew Research Center. (2024). Teens and Video Games Today. Access: 19 June 2024 https://www.pewresearch.org/internet/2024/05/09/teens-and-video-games-today/
34. Rita, P., Vong, C., Pinheiro, F., & Mimoso, J. (2023). A sentiment analysis of Michelin-starred restaurants. European Journal of Management and Business Economics, 32(3), 276-295.
35. Ryan, R. M., Rigby, C. S. and Przybylski, A. (2006). The Motivational Pull of Video Games: A Self-Determination Theory Approach. Motivation And Emotion, 30, 344-360.
36. Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108.
37. Scharkow, M., Festl, R., Vogelgesang, J. and Quandt, T. (2015). Beyond the “Core-Gamer”: Genre Preferences And Gratifications in Computer Games. Computers in Human Behavior, 44, 293-298.
38. Sensor Tower. (2024). Top Charts. Access: 18 June 2024 https://app.sensortower.com/top-charts?category=allandcountry=USanddate=2024-09-28anddevice=iphoneandos=android
39. Stanworth, J. O., Yen, W. H., & Warden, C. A. (2022). Conflicted about online learning?: Using sentiment analysis to explore learner approach-avoidance motivation. Online Information Review, 47(2), 356-370.
40. Van Rossum, G. and Drake Jr, F. L. (1995). Python Tutorial. Amsterdam, The Netherlands: Centrum voor Wiskunde en Informatica.
41. Viñán-Ludeña, M. S., & de Campos, L. M. (2022). Discovering a tourism destination with social media data: BERT-based sentiment analysis. Journal of Hospitality and Tourism Technology, 13(5), 907-921.
42. Wu, J. H., Wang, S. C. and Tsai, H. H. (2010). Falling in Love With Online Games: The Uses And Gratifications Perspective. Computers in Human Behavior, 26(6), 1862-1871.
43. Yi, J., Lee, Y. and Kim, S. H. (2019). Determinants of Growth and Decline in Mobile Game Diffusion. Journal of Business Research, 99, 363-372.
44. Youm, D. and Kim, J. (2022). Text Mining Approach to Improve Mobile Role Playing Games Using Users’ Reviews. Applied Sciences, 12(12), 6243.
45. Yu, Y., Dinh, T., Yu, F. and Huynh, V. N. (2023). Understanding Mobile Game Reviews Through Sentiment Analysis: A Case Study of PUBGm. International Conference on Model and Data Engineering, 2-4 November 2023, Sousse, Tunusia. 102-115.