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Updated: Jul 16, 2025

Eye Tracking Young Children with Autism

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Published on: March 27, 2012

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

Mariem Haoues^1,2, Raouia Mokni^3,4

¹Department of Software Engineering, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia.

Peerj. Computer Science

|September 14, 2023

Summary

This summary is machine-generated.

Analyzing user feedback for Autism Spectrum Disorder (ASD) apps reveals how to enhance their services. Machine learning models accurately classify reviews, guiding improvements for autistic students and employees.

Keywords:

Autism spectrum disorder Autism-friendly environment Deep learning models LSTM Mobile apps RNN Sentiment analysis User feedback

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Area of Science:

Neurodevelopmental Disorders
Human-Computer Interaction
Applied Machine Learning

Background:

Autistic individuals often face employment and educational challenges.
Mobile applications for Autism Spectrum Disorder (ASD apps) aim to support daily living and condition management.
User feedback analysis is crucial for improving ASD app functionality and user experience.

Purpose of the Study:

To investigate the effectiveness of ASD apps in improving the quality of life for autistic students and employees.
To analyze user reviews of highly-ranked ASD apps to identify areas for service enhancement.
To provide data-driven recommendations for ASD app developers.

Main Methods:

Collected 97,051 user reviews from 13 ASD apps on Google Play and Apple App stores.
Classified reviews into negative, positive, and neutral sentiment categories.
Employed machine learning and deep learning models, specifically combining Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) models, for sentiment analysis.

Main Results:

Achieved high accuracy (96.58%) and Area Under the Curve (AUC) of 99.41% using combined RNN and LSTM models for review classification.
Identified key themes and sentiments within user feedback to pinpoint strengths and weaknesses of current ASD apps.
Demonstrated the efficacy of advanced machine learning techniques in analyzing large-scale user-generated data.

Conclusions:

User feedback analysis is a valuable tool for enhancing ASD applications.
Combined RNN-LSTM models offer superior performance in sentiment analysis of ASD app reviews.
Recommendations derived from this analysis can guide developers in upgrading ASD app services to better support autistic individuals.