AI System for Monitoring Mental Health from Text

Abstract

Mental health conditions often remain hidden because individuals hesitate to seek help or fail to recognize early symptoms. With people increasingly expressing their thoughts and emotions through digital communication, text has become a valuable source for understanding psychological wellbeing. This study presents an AI-driven system designed to monitor mental health indicators by analyzing textual input from sources such as social media posts, chat conversations, emails, and personal journals. The system combines natural language processing techniques with machine learning models to identify linguistic patterns linked to stress, anxiety, depression, and emotional decline. It focuses on extracting features related to sentiment, emotion intensity, cognitive distortions, writing style shifts, and negative behavioral cues. These signals are fed into classification models that learn to distinguish between healthy and high-risk mental states.The system architecture integrates a preprocessing pipeline for cleaning and normalizing informal text, a feature extraction layer for semantic and syntactic patterns, and supervised learning algorithms for prediction. It uses methods such as word embeddings, transformer-based language models, and sentiment scoring to enhance detection accuracy. A risk-scoring module aggregates model outputs and generates an interpretable mental-health profile that highlights significant changes over time. The study also evaluates the system on benchmark mental health datasets and user-generated text, demonstrating strong performance in identifying early mental distress. The goal is to support early intervention by providing researchers and clinicians with a non-intrusive tool that can monitor psychological signals at scale. While the system is not a replacement for professional diagnosis, it offers a practical way to assist mental health assessment and promote timely support for individuals showing signs of emotional difficulty. Overall, the study contributes an effective and ethically aligned framework capable of identifying mental health indicators from text with high reliability. It highlights the potential of AI to enhance early intervention efforts, support mental health professionals, and assist in continuous monitoring when traditional clinical access is limited.