Machine Learning-Based Sentiment Analysis for Suicide Prevention and Mental Health Monitoring in Educational Institutions

Mental health issues and suicidal tendencies among students are growing concerns in educational institutions. Early detection and intervention are crucial for prevention, yet traditional methods often rely on self-reporting and manual assessments, which may be delayed or inaccurate. This study explores the use of machine learning-based sentiment analysis to monitor students' emotional well-being and identify signs of distress. By analyzing text from social media, academic forums, and communication platforms, Natural Language Processing (NLP) and deep learning models can detect negative sentiment patterns indicative of mental health risks. The proposed approach aims to develop an intelligent, real-time monitoring system for early intervention and personalized support. The findings contribute to AI-driven solutions for mental health awareness and suicide prevention in educational settings.The model accurately detects mental distress and suicidal tendencies using NLP and deep learning, enabling early intervention.Future work can integrate multimodal data, real-time monitoring, and AI-driven interventions for improved mental health support.