Abstract
Suicide is a major, preventable public health problem. Particularly the problem is critical for young people. In Russia every year thousands of teenagers commit suicide. In most of the cases it can be prevented if a risky state is detected. Nowadays internet becomes a major way of communication, mainly in the text form. Therefore we suggest a method to detect a tendency to suicide based on text messages. Our main approach is to study indicators of such condition and based on it use machine learning approach to build a classifier that could determine, whether the person is about to commit a suicide. Our experiments are based on the analysis of texts of Russian writers for past 100 years that committed suicide.
Highlights
Suicide poses a serious public health problem worldwide
In Russia this problem is urgent among young people
We presented an approach to suicide detection in texts using linguistic characteristics and machine learning
Summary
Suicide poses a serious public health problem worldwide. Every year around 1 million deaths and 10 million attempts occurs due to that [1, 2]. We could attempt to determine using text analysis: emotional state of the person; specific verbal suicidal signs. In order to improve the efficiency of determining emotional state we proposed to use following advanced features: share of all punctuation signs in the size of the text; number of specific “mood” signs used: three dots, exclamation marks, question marks; average and maximum length of the words; share of each part of speech (obtained from MyStem annotation); various phonetic characteristics (e.g. individual letters, bigrams, trigrams and quantitative features like word length, maximum sequential consonant length in a word, etc.) [15]. The second part of analysis is about specific verbal signs that could indicate suicide This part was partly covered by words and word combinations as features, such as burden, death, pain, despair, etc. 4. Experiments We classified individual texts into two classes: texts written by a person committed suicide and not. A text about death written by an author who did not committed suicide The last model showed the best results, all the results are presented in the Table 1 117
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