Four Supervised Models for Identifying Suicide Indicators in Text Data

Abstract

The increasing prevalence of mental health disorders like depression and anxiety often leads to suicide, with individuals frequently expressing such thoughts on social media. Utilizing machine learning techniques to analyze social media texts would work towards preventing these outcomes, even though predicting suicide risk remains a challenge. In this study machine learning classifiers were developed aiming to detect suicide indicators using the Kaggle Suicide and Depression Detection dataset (Komati, N. Suicide Watch). Four models—Multinomial Naive Bayes, Gradient Boost Machine (GBM), Random Forest and Support Vector Machines—were tested, yielding promising results: Among the four models presented here SVM with a 0.95 Precision and 0.94 F1 score showed the best results.

Keywords: Suicide, Supervised Learning, Machine Learning, Naïve Bayes, Gradient Boosting Machine, Random Forest, Support Vector Machines.