Journal of Neonatal Surgery ISSN(Online): 2226-0439 Vol. 14, Issue 5s (2025) https://www.jneonatalsurg.com pg. 396 Journal of Neonatal Surgery | Year: 2025 | Volume: 14 | Issue: 5s Machine Learning-Based Sentiment Analysis for Suicide Prevention and Mental Health Monitoring in Educational Institutions Anas Habib Zuberi 1 , Ambreen Anees 2 , Naziya Anjum 3 , Ajaz Husain Warsi 4 , Pervez Rauf Khan 5 , Sudheer Kumar singh 6 , Nagendra Kumar Singh 7 , Ranjana Singh 8 , Syed Hauider Abbas 9 , Rahul Ranjan 10 1 PhD Scholar Cum Assistant Professor, Email ID: ahzuberi.wp@gmail.com 2 PhD Scholar cum Assistant Professor,Email ID: ambreenanees0092@gmail.com 3 PhD scholar cum Assistant professor, Email ID: syednazia91@gmail.com 4 PhD Scholar Cum Assistant Professor Integral University Lucknow. Email ID: ajazwarsi01@gmail.com 5 PhD scholar cum Assistant professor, Email ID: pervezrauf@gmail.com 6 Associate Professor, Department of Computer Science and Engineering , Galgotias University, Greater Noida. Email ID: sudheerhbtisomvansi@gmail.com 7 Assistant Professor,Department of Computer Science,ERA University, Lucknow. Email ID: Nksingh444@gmail.com 8 Associate Professor, School of Humanities and Social Sciences, Sharda University, Greater Noida, Uttar Pradesh, India. Email ID: Ranjana.june@gmail.com 9 Faculty, CSE, Integral University. Email ID: abbasphdcse@gmail.com 10 Rahul Ranjan, Assistant Professor,CSE, Integral University. Email ID: rrtiwari88@gmail.com 00Cite this paper as: Anas Habib Zuberi, Ambreen Anees, Naziya Anjum, Ajaz Husain Warsi, Pervez Rauf Khan, Sudheer Kumar singh, Nagendra Kumar Singh, Ranjana Singh, Syed Hauider Abbas, Rahul Ranjan, (2025) Efficacy of structured exercise protocol on functional capacity, HbA1c, and waist-hip ratio in post-myocardial infarction subjects with diabetes mellitus. Journal of Neonatal Surgery, 14 (5s), 396-405. ABSTRACT 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. Keywords: Sentiment analysis, Suicide prevention, Mental health monitoring, Machine learning, NLP 1. INTRODUCTION Mental health has become a significant global concern, particularly among students in educational institutions who face immense academic, social, and personal pressures. The increasing cases of stress, anxiety, depression, and suicidal tendencies among students highlight the need for early detection and intervention strategies. Traditionally, mental health issues are identified through self-report surveys, clinical assessments, and counseling sessions. However, these methods often suffer