Article
Suicidal Content Detection From Social Media Post
Suicidal ideation detection from social media platforms has become an important research area for identifying individuals who may be at risk and enabling timely intervention. This project focuses on analyzing Twitter posts to detect signs of suicidal thoughts using Natural Language Processing (NLP) techniques implemented in Python with the Natural Language Toolkit (NLTK). The proposed system processes textual data by performing preprocessing tasks such as tokenization, stop-word removal, and feature extraction, followed by sentiment analysis and classification. By examining linguistic patterns, emotional expressions, and contextual cues within tweets, the model categorizes posts into high, medium, or low-risk levels of suicidal ideation. The approach is designed to uncover subtle indicators of psychological distress that may otherwise go unnoticed. This enables healthcare professionals, counselors, and support organizations to take appropriate preventive actions and provide assistance to vulnerable individuals. The project aims to strengthen mental health monitoring, improve early risk identification, and offer a scalable, automated solution that contributes to suicide prevention and the promotion of overall well-being.
Full Text Attachment
