3.8 Article

Thematic Analysis on User Reviews for Depression and Anxiety Chatbot Apps: Machine Learning Approach

Journal

JMIR FORMATIVE RESEARCH
Volume 6, Issue 3, Pages -

Publisher

JMIR PUBLICATIONS, INC
DOI: 10.2196/27654

Keywords

anxiety; depression; chatbots; conversational agents; topic modeling; latent Dirichlet allocation; thematic analysis; mobile phone

Funding

  1. Qatar National Research Fund [NPRP12S-0303-190204]

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This study conducted a thematic analysis of user reviews of anxiety and depression chatbot apps to explore users' opinions, satisfaction, and attitudes towards these apps. The analysis revealed different themes for positive and negative reviews, such as confidence building, analysis and consultation, caring, and ease of use. The study also provided a methodological workflow for future analysis of app review comments.
Background: Anxiety and depression are among the most commonly prevalent mental health disorders worldwide. Chatbot apps can play an important role in relieving anxiety and depression. Users' reviews of chatbot apps are considered an important source of data for exploring users' opinions and satisfaction. Objective: This study aims to explore users' opinions, satisfaction, and attitudes toward anxiety and depression chatbot apps by conducting a thematic analysis of users' reviews of 11 anxiety and depression chatbot apps collected from the Google Play Store and Apple App Store. In addition, we propose a workflow to provide a methodological approach for future analysis of app review comments. Methods: We analyzed 205,581 user review comments from chatbots designed for users with anxiety and depression symptoms. Using scraper tools and Google Play Scraper and App Store Scraper Python libraries, we extracted the text and metadata. The reviews were divided into positive and negative meta-themes based on users' rating per review. We analyzed the reviews using word frequencies of bigrams and words in pairs. A topic modeling technique, latent Dirichlet allocation, was applied to identify topics in the reviews and analyzed to detect themes and subthemes. Results: Thematic analysis was conducted on 5 topics for each sentimental set. Reviews were categorized as positive or negative. For positive reviews, the main themes were confidence and affirmation building, adequate analysis, and consultation, caring as a friend, and ease of use. For negative reviews, the results revealed the following themes: usability issues, update issues, privacy, and noncreative conversations. Conclusions: Using a machine learning approach, we were able to analyze >= 200,000 comments and categorize them into themes, allowing us to observe users' expectations effectively despite some negative factors. A methodological workflow is provided for the future analysis of review comments.

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