MENTAL HEALTH ASSISTANT: AI-BASED DEPRESSION ANALYSIS AND COMMUNICATION SYSTEM

Abstract

Mental health disorders, particularly depression, have emerged as a significant global health concern affecting individuals across various age groups. Early identification and timely intervention are essential to mitigate the adverse psychological and social impacts associated with depression. However, social stigma and limited access to professional mental health services often delay diagnosis and treatment. This paper presents an AI-based Mental Health Assistant designed to analyse depressive symptoms and provide intelligent, interactive communication support in a confidential and accessible manner. The proposed system employs Natural Language Processing (NLP) and Machine Learning (ML) techniques to analyse user-generated textual input and identify emotional and behavioural patterns indicative of depressive tendencies. The architecture consists of data preprocessing, feature extraction, model training, sentiment classification, and a conversational interface module. The trained model evaluates user responses to estimate depression risk levels and generates contextaware supportive feedback, coping strategies, and recommendations for professional consultation when required. Experimental evaluation demonstrates that the system achieves reliable classification performance while maintaining empathetic and responsive communication. The developed solution provides a scalable, cost-effective, and easily deployable platform that can complement traditional counselling services. The proposed AI-driven framework contributes to early detection, enhanced accessibility, and continuous mental health support through intelligent digital assistance