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

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.

Keywords: Weverse| Topic modeling| LDA| Fan community platform| Communication

Van Ho, T. T., Noh, M. J., Lee, Y. N., & Kim, Y. S. (2024). Analyzing User Feedback on a Fan Community Platform'Weverse': A Text Mining Approach. 스마트미디어저널, 13(6), 62-71.

https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE11979888