Analyzing schizophrenic texts from social media through machine learning and natural language processing

Abstract

Schizophrenia is a destructive personality disorder where people may develop harmful symptoms if not diagnosed promptly. The research focuses on identifying language patterns indicative of schizophrenic-prone texts in online communication and intends to contribute to the development of early intervention techniques in mental health using ML and NLP methods. The study has utilized an existing dataset to examine language patterns associated with schizophrenia in social media posts. Various ML, BERT and RNN models have been used to predict textual data suggestive of schizophrenia. The analysis shows excellent results, with the DistilBERT achieving the highest accuracy rates of 97%, GRU acquiring 91% accuracy, and the logistic regression achieving 93% accuracy respectively.