Text Mining Analysis of User Experience in C2C E-Commerce Based on Reviews of the Flea Market on App Mercari

Abstract

This study investigates user experience on the Japanese C2C platform, Mercari, through text mining analysis of App Store reviews. Using Python for data collection and KH Coder for statistical text analysis, 2,976 valid reviews were examined to uncover factors affecting user satisfaction. Keywords, co-occurrence networks, and correspondence analysis identified key areas such as item listing, purchasing processes, fees, customer service, and platform management. Positive ratings highlighted smooth transactions, engaging campaigns, and reliable service as strengths that maintained user interest. In contrast, negative reviews cited issues like inadequate customer support, high fees, and app functionality problems, leading to dissatisfaction and reduced trust. The co-occurrence network analysis depicted user concerns, particularly about fees, security, and transaction transparency. Correspondence analysis showed emotional responses driving ratings: lower scores linked to poor customer support and technical issues, while higher ratings were connected to promotions and positive shopping experiences. Cross-tabulation analysis revealed significant differences in user satisfaction among categories, emphasizing the need for operational improvements, especially in customer service and fee structures. The results indicate that enhancing customer support, refining fee policies, and ensuring smooth app usability are essential to building trust and improving satisfaction. These insights provide valuable direction for C2C platforms seeking to optimize user experience and maintain a competitive edge in the market.