Hybrid Deep Learning for ASD Prediction: Integrating Sentiment Analysis and Character-Level Embedding from Tweets and Clinical Data

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder considered by challenges in communication, social interaction, and behavior. Early and accurate detection remains a challenge due to subjective clinical assessments. This work presents an innovative hybrid framework that incorporates sentiment analysis from social media data with deep learning techniques applied to clinical data to enhance ASD prediction. Leveraging Twitter sentiment, a Long Short-Term Memory (LSTM) model captures public emotional and behavioral patterns, while clinical data undergoes deep learning-based character-level embedding using a Convolutional Neural Network (CNN) with transfer learning. The fusion of these models through a weighted sum mechanism results in a comprehensive ASD detection system. Experimental outcomes demonstrate an accuracy of 99.3% with a loss of 1.45%, significantly outperforming traditional diagnostic approaches. Visual analyses further validate key correlations between assessment scores and ASD diagnoses. Despite limitations such as misinformation in social media and the need for larger clinical datasets, the proposed approach highlights the potential of integrating social and clinical perspectives for improved ASD diagnosis. Future work will explore broader social media sources and advanced deep learning architectures to refine the model, ultimately contributing to early intervention and progressed results for individuals with ASD.