Mental health detection using sentiment analysis

Mental health challenges such as stress, anxiety, and depression will continue to become increasingly prevalent in modern society, making early identification essential for promoting emotional well-being. This project will present an advanced Mental Health Detection System using Sentiment Analysis to analyze user-generated text and identify emotional states, including Positive, Stress, Anxiety, and Depression. The system will employ Natural Language Processing (NLP) techniques to preprocess textual data and will convert it into meaningful numerical representations using word embeddings. A deep learning model based on Long Short-Term Memory (LSTM) will be utilized to capture contextual information and enhance classification accuracy. In addition to predicting emotional states, the system will provide confidence scores and personalized recommendations. The application will be implemented as a web-based platform to deliver real-time results.