Mental Health Analysis and Feedback System Using NLP and ML

Mental health concerns have become one of the most pressing global issues of the 21st century, affecting individuals across age groups, geographies, and professions. With the rising prevalence of anxiety, stress, and depression, early detection and timely feedback systems are essential to prevent escalation. This research presents the Mental Health Analysis and Feedback System Using Natural Language Processing (NLP) and Machine Learning (ML)—a web-based solution capable of analyzing user-generated text to identify emotional states and potential signs of psychological distress. By integrating NLP-based text preprocessing, emotion classification using ML models, and an intelligent feedback mechanism, the proposed framework facilitates an accessible and scalable method for mental health assessment. The system leverages publicly available datasets, including GoEmotions and Emotions Dataset for NLP, to train robust classification models capable of multi-label emotion detection. The output from these models is used to generate empathetic, non-clinical feedback for users. This study contributes toward the intersection of artificial intelligence and digital health, demonstrating how computational linguistics and supervised learning techniques can be harnessed for social good.