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JOURNALS // Program Systems: Theory and Applications // Archive

Program Systems: Theory and Applications, 2019 Volume 10, Issue 3, Pages 129–159 (Mi ps348)

Artificial Intelligence, Intelligent Systems, Neural Networks

Tools for the analysis of the depressed state and personality traits of a person

N. V. Kiselnikovaa, E. A. Kuminskayaa, A. V. Latyshevb, V. P. Fralenkoc, M. V. Khachumovd

a RAE Psychological Institute
b RI Tehnologii
c Ailamazyan Program Systems Institute of Russian Academy of Sciences
d Peoples' Friendship University of Russia

Abstract: The analysis of works dedicated to the identification of a stable relationship between personality traits and a person’s depression is carried out according to a complex of information available in social networks. The importance of automated problem solving follows from the need to timely detect signs of depression as a widespread mental illness to take measures for its prevention and treatment in the early stages.
The article discusses the issues of building mechanisms for identifying patterns and building modern tools for analyzing social network data for conducting scientific research in the subject area. As tools for identifying depression, it is proposed to apply contemporary methods of automatic analysis of web pages, formalize the identification of destructive information on psychologists’ proposals, test hypotheses about the presence of correlation links, automatically classify text-graphic information using an artificial neural network device in combination with semantic and psychological methods data analysis. Based on the studies performed, we found a significant correlation between various gradations of depression and some personality traits, as well as the presence of a stable correlation between the personality traits of the Big Five. (In Russian).

Key words and phrases: personality traits, Big Five, social network, depression, Big Data, automatic analysis, web page, correlation, artificial neural network, psychological portrait.

UDC: 159.9.072.5:004.89
BBK: 88.91:32.813.52

MSC: Primary 68T99; Secondary 62P15, 62N99

Received: 13.06.2019
12.09.2019
Accepted: 30.09.2019

DOI: 10.25209/2079-3316-2019-10-3-129-159



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