ORIGINAL RESEARCH article
Front. Comput. Sci.
Sec. Human-Media Interaction
Volume 7 - 2025 | doi: 10.3389/fcomp.2025.1569058
Sentiment Analysis of User Reviews: Exploring Duolingo and Babbel in English
Provisionally accepted
- 1Riphah International University (Lahore), Lahore, Pakistan
- 2COMSATS University Islamabad, Lahore Campus, Lahore, Punjab, Pakistan
- 3University of Lahore, Lahore, Punjab, Pakistan
- 4Faculty of Arts and Sciences, Near East University, Nicosia, Cyprus
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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-centred 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.
Keywords: Mobile Language Learning Applications (MLLAs), sentiment analysis, user feedback, Babbel, Duolingo
Received: 07 Mar 2025; Accepted: 09 Jun 2025.
Copyright: © 2025 Tabssam, Chattha, Javeed and Hayat. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Husnat Ahmed Tabssam, Riphah International University (Lahore), Lahore, Pakistan
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