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Linguistic model for diagnostics of suicidal behavior

T.A. Litvinova, P.V. Seredin, O.A. Litvinova, O.V. Zagorovskaya
80,00 ₽  


Litvinova Tatiana A.,

PhD in Philology, researcher,

Head of Lab of Corpus Sociolinguistics and Authorship Profiling

(RusProfiling Lab)

Voronezh State Pedagogical University



Seredin Pavel V.,

Dr.Sci. in Physics and Mathematics,

Associate professor of Department of Solid State and Nanostructures

Voronezh State University



Litvinova Olga A.,

Assistant lecturer in English Language Department

Voronezh State Pedagogical University



Zagorovskaya Olga V.,

Dr.Sci. in Philology, Professor,

Head of department in Russian Language, Modern Russian and Foreign Literature

Voronezh State Pedagogical University



Suicide is among the three most common causes of mortality in the group of young adults (15 to 24 years), but methods of diagnosing the propensity of personality to suicide has not developed so far. One of the promising areas of research in this area is a quantitative analysis of the speech. The foreign scientists use automatic text processing methods (natural language processing) and machine learning methods to study texts by suicides (mainly dying notes) and to built models to classify text as belonging or not belonging to suicides. However, for the development of techniques to identify individuals who are prone to suicide, it is necessary to analyze not only the suicide notes (usually a small amount of text), but also other texts by suicides. The aim of present paper is to construct a mathematical model that allows to classify a text as belonging or not belonging to suicide on the basis of the numerical values of linguistic parameters. The resulting model showed 67.5% accuracy.

Keywords: text classification methods, statistical methods in linguistics, mathematical model, predictors of suicide, corpus, computational linguistics.




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