Thematic Analysis on User Reviews for Depression and Anxiety Chatbot Apps: Machine Learning Approach
<h3>Background</h3><p dir="ltr">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 o...
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2022
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| _version_ | 1864513519151480832 |
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| author | Arfan Ahmed (17541309) |
| author2 | Sarah Aziz (17541312) Mohamed Khalifa (6844124) Uzair Shah (15740699) Asma Hassan (17058012) Alaa Abd-Alrazaq (17430900) Mowafa Househ (9154124) |
| author2_role | author author author author author author |
| author_facet | Arfan Ahmed (17541309) Sarah Aziz (17541312) Mohamed Khalifa (6844124) Uzair Shah (15740699) Asma Hassan (17058012) Alaa Abd-Alrazaq (17430900) Mowafa Househ (9154124) |
| author_role | author |
| dc.creator.none.fl_str_mv | Arfan Ahmed (17541309) Sarah Aziz (17541312) Mohamed Khalifa (6844124) Uzair Shah (15740699) Asma Hassan (17058012) Alaa Abd-Alrazaq (17430900) Mowafa Househ (9154124) |
| dc.date.none.fl_str_mv | 2022-03-11T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.2196/27654 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Thematic_Analysis_on_User_Reviews_for_Depression_and_Anxiety_Chatbot_Apps_Machine_Learning_Approach/25624224 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Health sciences Health services and systems anxiety depression chatbots conversational agents topic modeling latent Dirichlet allocation thematic analysis mobile phone |
| dc.title.none.fl_str_mv | Thematic Analysis on User Reviews for Depression and Anxiety Chatbot Apps: Machine Learning Approach |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Background</h3><p dir="ltr">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.</p><h3>Objective</h3><p dir="ltr">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.</p><h3>Methods</h3><p dir="ltr">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.</p><h3>Results</h3><p dir="ltr">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.</p><h3>Conclusions</h3><p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: JMIR Formative Research<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.2196/27654" target="_blank">https://dx.doi.org/10.2196/27654</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_db2786b3979d5d5c35568551b55b71a3 |
| identifier_str_mv | 10.2196/27654 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25624224 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Thematic Analysis on User Reviews for Depression and Anxiety Chatbot Apps: Machine Learning ApproachArfan Ahmed (17541309)Sarah Aziz (17541312)Mohamed Khalifa (6844124)Uzair Shah (15740699)Asma Hassan (17058012)Alaa Abd-Alrazaq (17430900)Mowafa Househ (9154124)Health sciencesHealth services and systemsanxietydepressionchatbotsconversational agentstopic modelinglatent Dirichlet allocationthematic analysismobile phone<h3>Background</h3><p dir="ltr">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.</p><h3>Objective</h3><p dir="ltr">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.</p><h3>Methods</h3><p dir="ltr">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.</p><h3>Results</h3><p dir="ltr">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.</p><h3>Conclusions</h3><p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: JMIR Formative Research<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.2196/27654" target="_blank">https://dx.doi.org/10.2196/27654</a></p>2022-03-11T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.2196/27654https://figshare.com/articles/journal_contribution/Thematic_Analysis_on_User_Reviews_for_Depression_and_Anxiety_Chatbot_Apps_Machine_Learning_Approach/25624224CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/256242242022-03-11T03:00:00Z |
| spellingShingle | Thematic Analysis on User Reviews for Depression and Anxiety Chatbot Apps: Machine Learning Approach Arfan Ahmed (17541309) Health sciences Health services and systems anxiety depression chatbots conversational agents topic modeling latent Dirichlet allocation thematic analysis mobile phone |
| status_str | publishedVersion |
| 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 | Health sciences Health services and systems anxiety depression chatbots conversational agents topic modeling latent Dirichlet allocation thematic analysis mobile phone |