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

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 satisfa...

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Detalles Bibliográficos
Autores principales: Ahmed, Arfan, Aziz, Sarah, Khalifa, Mohamed, Shah, Uzair, Hassan, Asma, Abd-Alrazaq, Alaa, Househ, Mowafa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8956988/
https://www.ncbi.nlm.nih.gov/pubmed/35275069
http://dx.doi.org/10.2196/27654
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author Ahmed, Arfan
Aziz, Sarah
Khalifa, Mohamed
Shah, Uzair
Hassan, Asma
Abd-Alrazaq, Alaa
Househ, Mowafa
author_facet Ahmed, Arfan
Aziz, Sarah
Khalifa, Mohamed
Shah, Uzair
Hassan, Asma
Abd-Alrazaq, Alaa
Househ, Mowafa
author_sort Ahmed, Arfan
collection PubMed
description 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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spelling pubmed-89569882022-03-27 Thematic Analysis on User Reviews for Depression and Anxiety Chatbot Apps: Machine Learning Approach Ahmed, Arfan Aziz, Sarah Khalifa, Mohamed Shah, Uzair Hassan, Asma Abd-Alrazaq, Alaa Househ, Mowafa JMIR Form Res Original Paper 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. JMIR Publications 2022-03-11 /pmc/articles/PMC8956988/ /pubmed/35275069 http://dx.doi.org/10.2196/27654 Text en ©Arfan Ahmed, Sarah Aziz, Mohamed Khalifa, Uzair Shah, Asma Hassan, Alaa Abd-Alrazaq, Mowafa Househ. Originally published in JMIR Formative Research (https://formative.jmir.org), 11.03.2022. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.
spellingShingle Original Paper
Ahmed, Arfan
Aziz, Sarah
Khalifa, Mohamed
Shah, Uzair
Hassan, Asma
Abd-Alrazaq, Alaa
Househ, Mowafa
Thematic Analysis on User Reviews for Depression and Anxiety Chatbot Apps: Machine Learning Approach
title Thematic Analysis on User Reviews for Depression and Anxiety Chatbot Apps: Machine Learning Approach
title_full Thematic Analysis on User Reviews for Depression and Anxiety Chatbot Apps: Machine Learning Approach
title_fullStr Thematic Analysis on User Reviews for Depression and Anxiety Chatbot Apps: Machine Learning Approach
title_full_unstemmed Thematic Analysis on User Reviews for Depression and Anxiety Chatbot Apps: Machine Learning Approach
title_short Thematic Analysis on User Reviews for Depression and Anxiety Chatbot Apps: Machine Learning Approach
title_sort thematic analysis on user reviews for depression and anxiety chatbot apps: machine learning approach
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8956988/
https://www.ncbi.nlm.nih.gov/pubmed/35275069
http://dx.doi.org/10.2196/27654
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