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Experimental Psychology (Russia)

Publisher: Moscow State University of Psychology and Education

ISSN (printed version): 2072-7593

ISSN (online): 2311-7036

DOI: https://doi.org/10.17759/exppsy

License: CC BY-NC 4.0

Started in 2008

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Possibilities of automatic text analysis in the task of determining the psychological characteristics of the author 109

Kovalev A.K.
Junior Researcher, Federal Research Center ‘Computer Science and Control’ of the Russian Academy of Sciences, Moscow, Russia
ORCID: https://orcid.org/0000-0001-7309-7382
e-mail: alexeykkov@gmail.com

Kuznetsova Y.M.
PhD in Psychology, Senior Researcher, Federal Research Center ‘Computer Science and Control’ of the Russian Academy of Sciences, Moscow, Russia
ORCID: https://orcid.org/0000-0001-9380-4478
e-mail: kuzjum@yandex.ru

Penkina M.Y.
Senior Lecturer of the Department of General Psychology, Moscow State University of Psychology and Education, Moscow, Russia
ORCID: https://orcid.org/0000-0001-7046-6963
e-mail: mpenkina@mail.ru

Stankevich M.A.
Junior Researcher, Federal Research Center ‘Computer Science and Control’ of the Russian Academy of Sciences, Moscow, Russia
ORCID: https://orcid.org/0000-0003-0705-5832
e-mail: maxastan95@gmail.com

Chudova N.V.
PhD in Psychology, Senior Researcher, Federal Research Center ‘Computer Science and Control’ of the Russian Academy of Sciences, Moscow, Russia
ORCID: https://orcid.org/0000-0002-3188-0886
e-mail: nchudova@gmail.com

Abstract
Using a tool for automatic text analysis and machine learning methods developed at the Federal Research Center ‘Computer Science and Control’ of the Russian Academy of Sciences, the first results are obtained in the task of identifying text parameters specific to people with certain psychological characteristics. The tool of corpus linguistic and statistical research, based on the use of relational-situational analysis, psycholinguistic indicators and dictionaries covering the vocabulary of emotional and rational assessment, allowed us to obtain values for 177 textual attributes of the essay written by 486 subjects. To obtain data on the severity of characterological and personality characteristics of the subjects, a number of psychological questionnaires were used. When processing the data, binary classification algorithms were used — the support vector method (SVM) and the Random Forest method. The results allow us to draw conclusions about the prospects of using some textual parameters in problems of population psychodiagnostics and the adequacy of the applied classification algorithms.

Keywords: automatic text analysis, personality characteristics, binary classification methods

Column: Psycholinguistics

DOI: https://doi.org/10.17759/exppsy.2020130111

For Reference

Funding

This work was partly supported by the Russian Foundation for Basic Research (project No. 17-29-02247 “Development of methods for diagnosing the spread of frustration in network discussions” and project No. 18-00-00233 “Methods for the integrated intellectual analysis of various types of information for social and humanitarian research in social media”).

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