AI-Based Mental Health Monitoring System Using Sentiment Analysis and Deep Learning

This paper presented MindScan, an AI-Based Mental Health Monitoring System using fine-tuned BERT and NLP to classify emotional states from natural language text. The proposed system achieved 88.4% classification accuracy across four mental health severity categories, outperforming the LSTM baseline by 7.2 percentage points, with the most significant gain observed on the clinically critical Severe Distress class.   The deployed Flask web application provides a free, accessible, and non-intrusive mental health screening tool operable through any web browser. The three-level alert system ensures appropriate safety responses including crisis helplines for high-risk cases. Complete source code is published on GitHub for transparency and academic reproducibility.   Future work will explore multilingual support for regional Indian languages using IndicBERT, multimodal analysis integrating voice tone features, federated learning for privacy-preserving model training, and clinical validation with licensed mental health professionals to establish diagnostic-grade reliability.

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