Twitter Data Analysis For Suicidal Intent

Authors: Vivek Nagargoje, Tushar Tayade, Prathmesh Dhamale, Kshitij Ghumare

Abstract: Early identification of suicidal intent on social media is vital for effective suicide prevention. This paper proposes a robust system for detecting suicidal intent from Twitter data using a Support Vector Machine (SVM) classifier as the core algorithmic component. The system pipeline includes text preprocessing, TF-IDF-based feature extraction, sentiment score enrichment, and SVM-based binary classification. The SVM model with RBF kernel is trained and evaluated on a labeled Twitter dataset, achieving an accuracy of 94.2%, precision of 92.5%, recall of 91.8%, and F1-Score of 93.3%. The proposed SVM-based approach offers a practical balance between classification accuracy and computational efficiency, making it well-suited for real-time deployment in mental health monitoring systems.

DOI: http://doi.org/10.5281/zenodo.20621545