Detection of Autism Spectrum Disorder (ASD) from Natural Language Text using BERT and ChatGPT Models

D may be caused by a combination of genetic and environmental factors, including gene mutations and exposure to toxins. People with ASD may also have trouble forming social relationships, have difficulty with communication and language, and struggle with sensory sensitivity. These difficulties can range from mild to severe and can affect a person's ability to interact with the world around them. Autism spectrum disorder (ASD) is a developmental disorder that affects people in different ways. But early detection of ASD in a child is a good option for parents to start corrective therapies and treatment. They can take action to reduce the ASD symptoms in their child. The proposed work is the detection of ASD in a child using a parent's dialog. The most popular Bert model and recent ChatGPT have been utilized to analyze the sentiment of each statement from parents for the detection of symptoms of ASD. The Bert model has been developed by the transformers which are the most popular in the natural language processing field whereas the ChatGPT model is a large language model (LLM). It is based on Reinforcement learning from human feedback (RLHF) that can able to generate the sentiment of the sentence, computer language codes, text paragraphs, etc. The sentiment analysis has been done on parents' dialog using the Bert model and ChatGPT model. The data has been prepared from various Autism groups on social sites and other resources on the internet. The data has been cleaned and prepared to train the Bert model and ChatGPT model. The Bert model is able to detect the sentiment of each sentence from parents. Any positive sentiment detection means parents should be aware of their children. The proposed model has given 83 percent accuracy according to the prepared data.