Mobile Learning Experience via Text Mining of App Store User Reviews

This study investigates the mobile learning experience in Duolingo via a text mining analysis of users' emotional arguments and their overall app ratings of Duolingo app store reviews. Sentiment analysis is employed to extract emotional arguments from 24,391 lines of user feedback of Duolingo from the Google Play store. Analyses explore the relationship between sentiment scores and users' rating scores. The results indicate that higher sentiment compound scores align with elevated Duolingo app ratings and negative sentiments uniquely affecting user ratings. This underscores the influential role of emotional arguments in shaping users' perceptions of their mobile learning experiences. Beyond quantitative assessment, this research emphasizes the qualitative dimension of user experience with mobile learning applications. The findings support the study of app store reviews as a meaningful source of information on mobile learning and demonstrate the importance of using sentiment analysis to explore users’ affective experience of mobile learning.