Sentiment analysis of user reviews: exploring Duolingo and Babbel in English language learning

This study investigates the dynamics of user feedback for two prominent mobile language learning applications, Babbel and Duolingo, through the lenses of Dynamic Systems Theory (DST) and Sociocultural Theory (SCT). By employing a mixed-methods approach that integrates sentiment analysis, network analysis, and linguistic features analysis, a dataset of 190 user reviews for each application collected from app review platforms has been analysed. The research reveals distinct sentiment clusters, network metrics, and user engagement patterns, highlighting how sociocultural factors and user experiences shape perceptions of app functionality and effectiveness. Findings indicate that Duolingo users predominantly appreciate the gamified elements and simplicity of lessons, while Babbel users emphasize structured methodologies and cultural context. The application of network analysis using Gephi software elucidates the interconnectedness of user sentiments, identifying influential nodes and feedback trends that inform app design and development. This study contributes to the field of applied linguistics by demonstrating the potential of user feedback to enhance the usability and pedagogical efficacy of language learning technologies, ultimately advocating for a more learner-centered approach in the design of educational tools. The insights garnered from this research not only bridge the gap between linguistic theory and technological application but also underscore the importance of integrating user perspectives in the continuous evolution of language learning platforms.