본문 바로가기
KERD
논문KCI 등재

Analyzing User Feedback on a Fan Community Platform 'Weverse': A Text Mining Approach

1

조회

0

다운로드

0

인용

0

참고문헌

키워드

주제분류컴퓨터학

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.

Abstract

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.

참고문헌 (25)

  1. Kim, M. S., "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, 68, pp.232~243 (2017)
  2. "Hallyu (Korean Wave)"[URL]
  3. "About the Company"[URL]
  4. "Weverse"[URL]
  5. "Weverse service"[URL]
  6. "구글플레이 '2023 올해를 빛낸인기 앱' 후보 올라" (2023)[URL]
  7. "Fandom"[URL]
  8. Kim, M. S., "Effects of Online Fan Community Interactions on Well-Being and Sense of Virtual Community", Behavioral Sciences, 13(11), pp.897~ (2023)
  9. Ham, M., "Factors affecting the popularity of video content on live-streaming services : focusing on V Live, the South Korean live-streaming service", Sustainability, 12(5), pp.1784~ (2020)
  10. "K-pop fan service Universe to shut down, transfer to Dear U Bubble" (2023)[URL]
  11. Hong, S. Y., "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, 9(4), pp.451~ (2022)
  12. Hyun-Su Kim, "A Study on Entertainment Platform Service Evaluation Using IPA(Importance-Performance Analaysis) : Focusing on Weverse", Journal of the Korea Entertainment Industry Association, 16(7), pp.1~17 (2022)
  13. 장혜리, "A Study on the Fan Community Platform that supports Artists in the Untact Era", Master, pp.2021
  14. Machado Pereira, S, "Fan engagement strategies in the K-pop industry", Doctor, pp.2022
  15. Song, M, "A Study on the Business Model of a Fan Community Platform 'Weverse'", The International Journal of Advanced Smart Convergence, 10(4), pp.172~182 (2021)
  16. Kyung-Yur Lee, "Social Big Data Analysis of Perceptions and Issues of Hallyu Fandom", Journal of the Korea Entertainment Industry Association, 17(4), pp.1~16 (2023)
  17. "2020", 1
  18. "Google-Play-Scraper" (2024)[URL]
  19. "What is Gensim?" (2009)[URL]
  20. Hasan, M., "Normalized approach to find optimal number of topics in Latent Dirichlet Allocation(LDA)", Proceedings of International Conference on Trends in Computational and Cognitive Engineering: Proceedings of TCCE 2020, pp.341~354 (2021)
  21. Qomariyah, S., "Topic modeling twitter data using latent dirichlet allocation and latent semantic analysis", AIP conference proceedings, 2194(1) (2019)
  22. Röder, M., "Exploring the Space of Topic Coherence Measures", Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, pp.399~408 (2015)
  23. S. Syed, "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)[URL]
  25. Sievert, C., "LDAvis : A method for visualizing and interpreting topics", Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces, pp.63~70 (2014)

이 간행물의 다른 논문 (9건)