Automatic Identification of Suicide Notes from Linguistic and Sentiment Features

Psychological studies have shown that our state of mind can manifest itself in the linguistic features we use to communicate. Recent statistics in suicide prevention show that young people are increasingly posting their last words online. In this paper, we investigate whether it is possible to automatically identify suicide notes and discern them from other types of online discourse based on analysis of sentiments and linguistic features. Using supervised learning, we show that our model achieves an accuracy of 86.6%, outperforming previous work on a similar task by over 4%.