ArticleJMIR formative research2022
Thematic Analysis on User Reviews for Depression and Anxiety Chatbot Apps: Machine Learning Approach.
Article in JMIR formative research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.
- Exploring the Role of Mobile Apps for Insomnia in Depression: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Comparison of an AI Chatbot With a Nurse Hotline in Reducing Anxiety and Depression Levels in the General Population: Pilot Randomized Controlled Trial.JMIR human factors · 2025Trial
- Acceptability of a Conversational Agent-Led Digital Program for Anxiety: Mixed Methods Study of User Perspectives.JMIR human factors · 2025Article
- Mapping the mHealth Nexus: A Semantic Analysis of mHealth Scholars' Research Propensities Following an Interdisciplinary Training Institute.Applied sciences (Basel, Switzerland) · 2025Article
- Article
- "It happened to be the perfect thing": experiences of generative AI chatbots for mental health.Npj mental health research · 2024Article
- Review
- Understanding the Long Haulers of COVID-19: Mixed Methods Analysis of YouTube Content.JMIR AI · 2024Article
- Analyzing User Reviews of the First Digital Contraceptive: Mixed Methods Study.Journal of medical Internet research · 2023Article
- An Artificial Therapist (Manage Your Life Online) to Support the Mental Health of Youth: Co-Design and Case Series.JMIR human factors · 2023Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAnxiety 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.
objectiveThis 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.
methodsWe 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.
resultsThematic 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.
conclusionsUsing 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.