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Related Experiment Video

Updated: Jul 16, 2025

Eye Tracking Young Children with Autism
09:03

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Published on: March 27, 2012

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

Mariem Haoues1,2, Raouia Mokni3,4

  • 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
PubMed
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 disorderAutism-friendly environmentDeep learning modelsLSTMMobile appsRNNSentiment analysisUser 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.