Hybrid Model Approach for Sentiment Analysis in Mental Health Monitoring using NLP

Mental health awareness is essential; the rise of social media presents a unique opportunity to monitor emotional well-being on a large scale. Traditional mental health monitoring systems often struggle with the complexity and subtlety of human emotions expressed in online text. To address this, this research includes a study of various sentiment analysis algorithms. A study of models like Decision Trees, Naïve Bayes, BERT and logistic regression is done to identify patterns associated with mental health outcomes. By analyzing real-time social media data, research is done for categorizing posts to identify depressive, anxious, or positive sentiments, providing timely and actionable insights. Our methodology aims to enable mental health professionals and researchers to leverage social media as a tool for better understanding and addressing community mental health needs.