스마트미디어저널 (Smart Media Journal)

  • 제13권6호
  • /
  • Pages.62-71
  • /
  • 2024
  • /
  • 2287-1322(pISSN)
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  • 2288-9671(eISSN)
한국스마트미디어학회 (THE KOREAN INSTITUTE OF SMART MEDIA)
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Analyzing User Feedback on a Fan Community Platform 'Weverse': A Text Mining Approach

  • Thi Thao Van Ho (Department of Management Information Systems in Keimyung University) ;
  • Mi Jin Noh (Department of Business Big Data, Keimyung University) ;
  • Yu Na Lee (Keimyung University) ;
  • Yang Sok Kim (department of Management Information Systems, Keimyung University)
  • 투고 : 2024.05.03
  • 심사 : 2024.06.10
  • 발행 : 2024.06.28
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초록

This study applies topic modeling to uncover user experience and app issues expressed in users' online reviews of a fan community platform, Weverse on Google Play Store. It allows us to identify the features which need to be improved to enhance user experience or need to be maintained and leveraged to attract more users. Therefore, we collect 88,068 first-level English online reviews of Weverse on Google Play Store with Google-Play-Scraper tool. After the initial preprocessing step, a dataset of 31,861 online reviews is analyzed using Latent Dirichlet Allocation (LDA) topic modeling with Gensim library in Python. There are 5 topics explored in this study which highlight significant issues such as network connection error, delayed notification, and incorrect translation. Besides, the result revealed the app's effectiveness in fostering not only interaction between fans and artists but also fans' mutual relationships. Consequently, the business can strengthen user engagement and loyalty by addressing the identified drawbacks and leveraging the platform for user communication.

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참고문헌

  1. Kim, M. S., & Kim, H. M, "The effect of online fan community attributes on the loyalty and cooperation of fan community members: The moderating role of connect hours," Computers in Human Behavior, vol. 68, pp.232-243, Mar. 2017. 
  2. Hallyu (Korean Wave). https://www.korea.net/AboutKorea/Culture-and-the-Arts/Hallyu (accessed Feb., 10, 2024). 
  3. About the Company. https://en.weverse.co/about (accessed Feb., 10, 2024). 
  4. Weverse. https://play.google.com/store/apps/details?id=co.benx.weverse (accessed Feb., 10, 2024). 
  5. Weverse service. https://en.weverse.co/weverse (accessed Feb., 10, 2024). 
  6. 위버스, 구글플레이 '2023 올해를 빛낸 인기 앱' 후보 올라 (2023). https://hybecorp.com/kor/news/news/4157?companyCode=COM_BENX&page=0 (accessed Feb., 10, 2024). 
  7. Fandom. https://dictionary.cambridge.org/dictionary/english/fandom (accessed Feb., 10, 2024). 
  8. Kim, M. S., Wang, S., & Kim, S, "Effects of Online Fan Community Interactions on Well-Being and Sense of Virtual Community," Behavioral Sciences, vol. 13, no. 11, pp. 897, 2023. 
  9. Ham, M., & Lee, S. W, "Factors affecting the popularity of video content on live-streaming services: focusing on V Live, the South Korean live-streaming service," Sustainability, vol. 12, no. 5, pp. 1784, 2020. 
  10. K-pop fan service Universe to shut down, transfer to Dear U Bubble (2023). https://koreajoongangdaily.joins.com/20 23/01/11/entertainment/kpop/Universe-NCSoft/20230111105854122.html (accessed Feb., 10, 2024). 
  11. Hong, S. Y., & Kim, S. I, "A Study on User Experience Design of Fandom Platform Applications in the Media Entertainment Industry: Focused on Weverse and Universe," Journal of Digital Art Engineering and Multimedia, vol. 9, no. 4, pp. 451, 2022. 
  12. Hyun-Su Kim, Nam-Mi Kwak, & Da-Yeon Kim, "A Study on Entertainment Platform Service Evaluation Using IPA (Importance-Performance Analaysis): Focusing on Weverse," Journal of the Korea Entertainment Industry Association, vol. 16, no. 7, pp. 1-17, 2022. 
  13. 장혜리, "A Study on the Fan Community Platform that supports Artists in the Untact Era," Master's Thesis, 상명대학교, 2021. 
  14. Machado Pereira, S, "Fan engagement strategies in the K-pop industry," Doctoral dissertation, Scuola universitaria professionale della Svizzera italiana, 2022. 
  15. Song, M, "A Study on the Business Model of a Fan Community Platform 'Weverse'," The International Journal of Advanced Smart Convergence, vol. 10, no. 4, pp. 172-182, 2021. 
  16. Kyung-Yur Lee, & Sang-Hyeon Park, "Social Big Data Analysis of Perceptions and Issues of Hallyu Fandom," Journal of the Korea Entertainment Industry Association, vol. 17, no. 4, pp. 1-16, 2023. 
  17. Ko, H. K, "An analysis of YouTube comments on BTS using text mining," The Rhizomatic Revolution Review [20130613], vol. 1, pp. 1-10, 2020. 
  18. Google-Play-Scraper (2024). https://pypi.org/project/google-play-scraper/ (accessed Feb., 10, 2024). 
  19. What is Gensim? (2009). https://radimrehurek.com/gensim/intro.html (accessed Feb., 10, 2024). 
  20. Hasan, M., Rahman, A., Karim, M. R., Khan, M. S. I., & Islam, M. J, "Normalized approach to find optimal number of topics in Latent Dirichlet Allocation (LDA)," In Proceedings of International Conference on Trends in Computational and Cognitive Engineering: Proceedings of TCCE 2020, pp. 341-354, 2021. 
  21. Qomariyah, S., Iriawan, N., & Fithriasari, K, "Topic modeling twitter data using latent dirichlet allocation and latent semantic analysis," In AIP conference proceedings, vol. 2194, no. 1, Dec.2019. 
  22. Roder, M., Both, A. & Hinneburg, A, "Exploring the Space of Topic Coherence Measures," In Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, pp. 399-408, Feb. 2015. 
  23. S. Syed and M. Spruit, "Full-Text or Abstract? Examining Topic Coherence Scores Using Latent Dirichlet Allocation," 2017 IEEE International Conference on Data Science and Advanced Analytics, pp. 165-174, 2017. 
  24. Topic coherence pipeline (2009). https://radimrehurek.com/gensim/models/coherencemodel.html#gensim.models.coherencemodel.CoherenceModel (accessed Feb., 10, 2024). 
  25. Sievert, C., & Shirley, K., "LDAvis: A method for visualizing and interpreting topics," In Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces, pp. 63-70, 2014.