Suicidal Ideation Detection System using Hybrid Machine Learning and NLP Techniques

Every year, approximately 800,000 people lose their lives to suicide, underscoring the urgent need to identify individuals at risk. With the widespread use of social media, many individuals openly share their thoughts, including expressions of suicidal ideation, providing an opportunity for timely intervention. This study introduces an advanced system for detecting suicidal content on social media platforms using a combination of Natural Language Processing (NLP), Deep Learning, and Machine Learning techniques. The system leverages keyword-based detection, sentiment analysis, and contextual NLP methods to identify posts indicative of suicidal ideation. A hybrid approach is adopted, integrating Logistic regression,Random Forest, Support Vector Machine, Naive Bayes and Artificial Neural Networks (ANNs) to enhance text classification performance and improve detection accuracy. The model is trained on a carefully curated dataset of labeled posts and evaluated using metrics such as precision, recall, and F1-score, demonstrating significant improvements in the accurate detection of concerning content. This automated system is designed for deployment by social media platforms to flag potentially harmful posts, allowing mental health professionals to review and provide timely support. By enhancing early identification and intervention efforts, this system offers a valuable tool for reducing suicide risks and potentially saving lives. In cases where suicidal content is identified, the system prompts the user with a supportive questionnaire, text box, or chatbot interface to offer immediate assistance or guidance. This project contributes to mental health awareness by providing an automated, accurate, and accessible tool for early detection of suicidal ideation, facilitating timely intervention and support.