Prediction of Suicidal Behaviour among the users on Social Media using NLP and ML

suicide attempts. Our findings contribute to the growing field of suicide prevention through cutting-edge machine learning techniques applied to natural language data. According to our experimental results, the PM model outperformed other machine learning algorithms with the highest classification score. It achieved an impressive accuracy of 92% and an F1 score of 85%. These findings highlight the effectiveness and potential of the PM model for the task at hand. Suicides are happening more often these days. Social media messages and chats are the preferred means of expression for those who are committing suicide. Several studies have shown that it is possible to spot suicide suspects from their online conversations and posts on social media. Consequently, it is imperative to create a machine learning system for automatic early identification of suicide ideation or any abrupt changes in a user's behaviour by examining his or her posts and chats on social media such as Twitter, Instagram, WhatsApp, and Facebook. Our research is focused on significantly improving the performance of our model to accurately recognize early indications of suicide attempts with high precision, thus contributing to the prevention of such tragic events. To achieve this, we utilized advanced text pre-processing techniques and feature extraction approaches, including CountVectorizer and word embedding. Our study involved training both XGBoost and NLP models, which were rigorously evaluated using a substantial dataset of 34,100 samples. Furthermore, to assess real-world applicability, we conducted live tests of our model using tweets collected via the Stream lit python-based web interface tool. The results of our experiments are promising, demonstrating the potential of our approach in detecting early signs of distress and aiding in the timely intervention to prevent.