Stress Detection using NLP

This paper presents a novel web application that leverages advanced machine learning and natural language processing techniques to detect stress from textual data, such as social media posts or survey responses. The system employs robust text preprocessing methods—including stemming, stopword removal, and cleaning—and utilizes a Naive Bayes classifier to accurately categorize input text as “Stress” or “No Stress.” Developed using the Flask framework, the application offers an interactive platform where users can assess their stress levels, access curated stress-reduction tips, and communicate with administrators who manage user queries and resources. This integrated solution not only enables early detection and proactive management of stress but also increases awareness of its impact on mental health, providing practical support for individuals, mental health professionals, and organizations to enhance well-being and productivity.