WellMind: Real-Time Mental Health Tracking through NLP-Driven Behavioral Insights

Mental health conditions such as stress, anxiety, and depression often remain undetected due to the absence of continuous monitoring mechanisms and the limitations of conven-tional psychological assessments. With individuals increasingly expressing emotions through digital communication, linguistic patterns offer valuable indicators of underlying psychological states. This paper presents WellMind: Real-Time Mental Health Tracking through NLP-Driven Behavioral Insights, an intelligent system that leverages Natural Language Processing (NLP) and Machine Learning (ML) to analyze user-generated text and identify emotional patterns in real time. The proposed framework employs a fine-tuned transformerbased language model to perform sentiment classification, behavioral pattern detection, and mood fluctuation analysis. A scalable and lowlatency backend implemented using Node.js enables seamless data flow between the user interface, analytical engine, and database layer, while ensuring privacy through anonymized data handling. The system also incorporates an interactive dashboard that visualizes emotional trends and delivers personalized feedback to promote early awareness and psychological self-regulation. Experimental observations indicate reliable emotion detection and responsive feedback generation, demonstrating the system’s effectiveness in continuous mental health monitoring. The proposed solution offers a practical, privacy-conscious, and accessible approach to technologyassisted mental well-being support, with potential ap-plicability in personal health management and digital intervention platforms.