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

Abstract

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.

Uso Do Chatbot No Enfrentamento Da Ansiedade: Uma Revisão Integrativa

“…Sendo (21) . Consequentemente, os desenvolvedores devem trabalhar para corrigir os pontos fracos de seus aplicativos e investir mais em áreas que os usuários consideram importantes para gerenciar suas condições de sofrimento mental (21) Eventualmente, isso deve melhorar a adoção pública de aplicativos projetados para condições de saúde mental, como ansiedade e depressão.…”

“…Sendo (21) . Consequentemente, os desenvolvedores devem trabalhar para corrigir os pontos fracos de seus aplicativos e investir mais em áreas que os usuários consideram importantes para gerenciar suas condições de sofrimento mental (21) Eventualmente, isso deve melhorar a adoção pública de aplicativos projetados para condições de saúde mental, como ansiedade e depressão. Entre os temas positivos, descobrimos que facilidade de uso, fornecimento de suporte, boa conversa humana e proporcionar sentimentos de elevação do humor foram percebidos como indicadores de atributos desejados (21) .…”

Manage Your Life Online ('MYLO'): Co-design and case-series of an artificial therapist to support youth mental health.

“…"I had a little bit of an issue at first where I asked, like, I said something specific and it didn't understand, but once I was using it more, I understood, like, to use broader words, stuff like that." (Woman, [18][19][20][21]. Some participants also found it hard to explain their feelings, "I found it difficult when it would ask to kind of, like, explain, like, in a few more sentences what you were feeling because I'm not much of a talker" (Woman, [16][17].…”

“…Like, I don't want to expand on what I said because I've already just said it." (Woman, [18][19][20][21]. Some participants also had trouble understanding some of MYLO's questions, "I don't know, I sort of struggled with, like, the questions that MYLO asked though, like, I sort of struggled to understand most of them, like, what they were sort of wanting me to talk about, I guess."…”

“…Recent studies focusing on the user experience to identify ways to improve the uptake and engagement of mental health chatbots have generally found high user satisfaction [12,18]. Users have indicated that they value the interactive conversational approach and appear to build a relationship with the chatbots akin to that of a human therapist or a friend [12,9].…”