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

• 영문명: Analyzing User Feedback on a Fan Community Platform 'Weverse': A Text Mining Approach
• 발행기관: 한국스마트미디어학회
• 저자명: Thi Thao Van Ho Mi Jin Noh Yu Na Lee Yang Sok Kim
• 간행물 정보: 『스마트미디어저널』Vol13, No.6, 62~71쪽, 전체 10쪽
• 주제분류: 공학 > 컴퓨터학
• 파일형태: PDF
• 발행일자: 2024.06.28

영문 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.

목차

Ⅰ. INTRODUCTION

Ⅱ. RELATED WORK

Ⅲ. METHODOLOGY

Ⅳ. RESULTS

Ⅴ. CONCLUSION

REFERENCES

키워드

Weverse Topic modeling LDA Fan community platform Communication