Decoding User Sentiments and Styles: A Comparative Analysis of Duolingo and Babbel through Appraisal Theory and TAM

Abstract

This study performs a comparative analysis of user sentiments and stylistic expressions in feedback reviews for two prominent mobile-assisted language learning (MALL) applications, Duolingo and Babbel, utilising Appraisal Theory (Martin and White, 2005) and the Technology Acceptance Model (Davis,1989). With the rapid growth of language learning platforms, understanding user experiences is crucial for enhancing application design and functionality. By employing a mixed methods approach, including qualitative and quantitative analyses of over 190 user reviews for each app from public domains, the research identifies trends in user satisfaction, areas of dissatisfaction, and stylistic patterns in user feedback. The findings reveal significant differences in user engagement and preferences between the two platforms, highlighting the importance of gamification in Duolingo and Babbel's structured approach. This study underscores the value of sentiment and stylistic analysis in informing the development of effective language learning applications that cater to diverse learner needs.