Insights from User Reviews to Improve Suicide Prevention Apps: A Machine Learning and Thematic Analysis-Based Approach

Abstract

In this study, we conducted a comprehensive evaluation of user reviews of suicide prevention apps using machine learning (ML) and thematic analysis to assess usability and user experience. Given the critical public health issue of suicide, mobile applications have emerged as potential intervention tools. However, user perspectives are underexplored. Our analysis encompassed user reviews from 39 suicide prevention apps, totaling 110,338 reviews. We employed natural language processing for sentiment analysis and implemented five ML classifiers with Random Forest achieving the highest F1 score (87.19%). The thematic analysis revealed seven negative themes including access to therapy and privacy concerns, alongside five positive themes such as app functionality and user engagement. This research quantitatively and qualitatively evaluates user sentiments, providing insights that can guide developers and policymakers in enhancing the design of suicide prevention apps, thereby addressing mental health challenges in the digital landscape.

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