SUCIDAL IDEATION DETECTION USING SOCIAL MEDIA

The objective of this project is to develop an automated system that can accurately identify potential suicidal ideation in social media posts. By leveraging advanced machine learning and deep learning techniques such as XGBoost, LSTM, and BERT, the system aims to classify social media content into suicidal and non-suicidal categories. The project focuses on pre-processing raw data, feature extraction using TF-IDF, and applying state-of-the-art algorithms to detect patterns indicative of suicidal thoughts. Ultimately, the goal is to provide a scalable, efficient, and real-time tool that can assist in mental health monitoring and early intervention.