Natural Language Processing-Driven Sentiment And Mental Health Analysis Using Social Media Healthcare Data

Abstract

The growing social isolation, emotional instability, digital addiction and psychological pressure of the current patterns of online interaction have led to a greater prevalence of mental health disorders. The constantly growing human emotional expression in social media posts, comments, behavioral interactions and textual communications forms a scale of digital behavioral data that can be analyzed intelligently through the use of artificial intelligence. In this paper, we outline a Natural Language Processing-based Sentiment and Mental Health Analysis Framework of Social Media Healthcare Data that provides a temporal emotional modeling, transformer-enhanced semantic learning, emotional volatility prediction, explainable linguistic interpretation, and psychological trajectory prediction to adapt to mental health. The suggested framework presents a behavior sentiment drift model that can follow the changes in emotions and long-term psychological development based on the contextual language embeddings produced as a result of communicating on social media. In contrast to the traditional sentiment classification systems, where research only considers the positive and negative polarity detection, the proposed approach examines emotional persistence, depressive language development, anxiety changes, and behavioral instability patterns of the temporal communication patterns. The framework also includes explainable artificial intelligence strategies of recognising psychologically swaying linguistic phrases and understandable emotional logic. Experimental analysis shows the great improvement in the prediction of depression, estimation of emotional volatility, contextual sentiment comprehension and the analysis of the real time psychological trajectory relative to the traditional machine learning and recurrent neural network models. The suggested framework thus offers scalable and smart healthcare analytics, which can be used to actively assess mental health and identify early signs of psychological risks through the use of social media healthcare communication.