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Toward an autism-friendly environment based on mobile apps user feedback analysis using deep learning and machine learning models

Autistic people are often disadvantaged in employment, education, etc. In fact, autistic students/employees face several challenges navigating and communicating with their superiors and colleagues. Mobile applications for people with Autism Spectrum Disorder (ASD apps for short) have been increasing...

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Detalles Bibliográficos
Autores principales: Haoues, Mariem, Mokni, Raouia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10495947/
https://www.ncbi.nlm.nih.gov/pubmed/37705619
http://dx.doi.org/10.7717/peerj-cs.1442
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author Haoues, Mariem
Mokni, Raouia
author_facet Haoues, Mariem
Mokni, Raouia
author_sort Haoues, Mariem
collection PubMed
description Autistic people are often disadvantaged in employment, education, etc. In fact, autistic students/employees face several challenges navigating and communicating with their superiors and colleagues. Mobile applications for people with Autism Spectrum Disorder (ASD apps for short) have been increasingly being adapted to help autistic people manage their conditions and daily activities. User feedback analysis is an effective method that can be used to improve ASD apps’ services. In this article, we investigate the usage of ASD apps to improve the quality of life for autistic students/employees based on user feedback analysis. For this purpose, we analyze user reviews suggested on highly ranked ASD apps for college students, and workers. A total of 97,051 reviews have been collected from 13 ASD apps available on Google Play and Apple App stores. The collected reviews have been classified into negative, positive, and neutral opinions. This analysis has been performed using machine learning and deep learning models. The best performances were provided by combining RNN and LSTM models with an accuracy of 96.58% and an AUC of 99.41%. Finally, we provide some recommendations to improve ASD apps to assist developers in upgrading the main services provided by their apps.
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spelling pubmed-104959472023-09-13 Toward an autism-friendly environment based on mobile apps user feedback analysis using deep learning and machine learning models Haoues, Mariem Mokni, Raouia PeerJ Comput Sci Bioinformatics Autistic people are often disadvantaged in employment, education, etc. In fact, autistic students/employees face several challenges navigating and communicating with their superiors and colleagues. Mobile applications for people with Autism Spectrum Disorder (ASD apps for short) have been increasingly being adapted to help autistic people manage their conditions and daily activities. User feedback analysis is an effective method that can be used to improve ASD apps’ services. In this article, we investigate the usage of ASD apps to improve the quality of life for autistic students/employees based on user feedback analysis. For this purpose, we analyze user reviews suggested on highly ranked ASD apps for college students, and workers. A total of 97,051 reviews have been collected from 13 ASD apps available on Google Play and Apple App stores. The collected reviews have been classified into negative, positive, and neutral opinions. This analysis has been performed using machine learning and deep learning models. The best performances were provided by combining RNN and LSTM models with an accuracy of 96.58% and an AUC of 99.41%. Finally, we provide some recommendations to improve ASD apps to assist developers in upgrading the main services provided by their apps. PeerJ Inc. 2023-08-09 /pmc/articles/PMC10495947/ /pubmed/37705619 http://dx.doi.org/10.7717/peerj-cs.1442 Text en © 2023 Haoues and Mokni 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, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Haoues, Mariem
Mokni, Raouia
Toward an autism-friendly environment based on mobile apps user feedback analysis using deep learning and machine learning models
title Toward an autism-friendly environment based on mobile apps user feedback analysis using deep learning and machine learning models
title_full Toward an autism-friendly environment based on mobile apps user feedback analysis using deep learning and machine learning models
title_fullStr Toward an autism-friendly environment based on mobile apps user feedback analysis using deep learning and machine learning models
title_full_unstemmed Toward an autism-friendly environment based on mobile apps user feedback analysis using deep learning and machine learning models
title_short Toward an autism-friendly environment based on mobile apps user feedback analysis using deep learning and machine learning models
title_sort toward an autism-friendly environment based on mobile apps user feedback analysis using deep learning and machine learning models
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10495947/
https://www.ncbi.nlm.nih.gov/pubmed/37705619
http://dx.doi.org/10.7717/peerj-cs.1442
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