토픽모델링을 이용한 러닝 앱 리뷰 감성분석
Sentiment Analysis of Running App Reviews Using Topic Modeling
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Purpose: This study aims to identify the reasons behind the lower ratings of ‘Nike Run Club’ in the Korean market and suggest enhancements to improve its competitive edge. Methods: A total of 17,764 Google Play Store reviews were collected and analyzed through web scraping. LDA topic modeling and sentiment analysis were applied, with the results integrated into the Kano model and PCSI index. Results: The LDA analysis identified four main topics: Error (App Stability), Workout Features, App Features/Compatibility, Beginner. These topics revealed key customer requirements, which were prioritized to compare and analyze Nike Run Club against competing applications. Conclusion: This study highlights essential requirements and preferences of running app users, offering insights that support future development and service improvements. These findings are relevant not only to the running app market but also have broader applicability across similar sectors.
Abstract
Purpose: This study aims to identify the reasons behind the lower ratings of ‘Nike Run Club’ in the Korean market and suggest enhancements to improve its competitive edge. Methods: A total of 17,764 Google Play Store reviews were collected and analyzed through web scraping. LDA topic modeling and sentiment analysis were applied, with the results integrated into the Kano model and PCSI index. Results: The LDA analysis identified four main topics: Error (App Stability), Workout Features, App Features/Compatibility, Beginner. These topics revealed key customer requirements, which were prioritized to compare and analyze Nike Run Club against competing applications. Conclusion: This study highlights essential requirements and preferences of running app users, offering insights that support future development and service improvements. These findings are relevant not only to the running app market but also have broader applicability across similar sectors.