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Experimental Psychology (Russia)
2020. Vol. 13, no. 1, 149–158
https://doi.org/10.17759/exppsy.2020130111
ISSN: 2072-7593 / 2311-7036 (online)

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

 
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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.

General Information

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

Journal rubric: Psycholinguistics

OpenAlex citations: 3

OpenAlex trends: Mental Health via Writing, Scientific Research and Philosophical Inquiry, Authorship Attribution and Profiling

Information about the work in OpenAlex

Number of citations: 3

Topics

Mental Health via Writing

This cluster of papers focuses on the analysis of psychological language in social media, particularly related to emotional disclosure, mental health, and expressive writing. It involves the use of natural language processing and machine learning techniques to detect and understand patterns of depression, suicidal ideation, and other mental health indicators in online communication.

Number of works: 32668  |  Total number of citations: 214760

Topic detailsв OpenAlex

Scientific Research and Philosophical Inquiry

This cluster of papers covers a wide range of topics related to information science and technology development, including the impact of digital technology, foresight methods, knowledge management, big data, innovation, media, ontology, and semantic search. The papers explore the intersection of information science with various fields such as economics, education, neuroscience, sociology, and environmental studies.

Number of works: 44007  |  Total number of citations: 112893

Topic detailsв OpenAlex

Authorship Attribution and Profiling

This cluster of papers focuses on authorship attribution, stylometry, and user profiling in text, utilizing techniques such as text classification, machine learning, and forensic linguistics to analyze gender differences and language use in social media. The research aims to identify authors of anonymous texts, predict demographic attributes from online content, and study the linguistic uniqueness of individuals across different genres and languages.

Number of works: 27659  |  Total number of citations: 130993

Topic detailsв OpenAlex

Work details in OpenAlex

Article type: scientific article

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

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”).

Published

For citation: Kovalev, A.K., Kuznetsova, Y.M., Penkina, M.Y., Stankevich, M.A., Chudova, N.V. (2020). Possibilities of automatic text analysis in the task of determining the psychological characteristics of the author. Experimental Psychology (Russia), 13(1), 149–158. (In Russ.). https://doi.org/10.17759/exppsy.2020130111

© Kovalev A.K., Kuznetsova Y.M., Penkina M.Y., Stankevich M.A., Chudova N.V., 2020

License: CC BY-NC 4.0

References

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Information About the Authors

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

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

Marina Y. Penkina, Senior Lecturer of the Department of General Psychology of the Institute of Experimental Psychology, Moscow State University of Psychology and Education, Moscow, Russian Federation, ORCID: https://orcid.org/0000-0001-7046-6963, e-mail: penkinamju@mgppu.ru

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

Natalia V. Chudova, Candidate of Science (Psychology), Senior Researcher, Federal Research Center ‘Computer Science and Control’ of the Russian Academy of Sciences, Moscow, Russian Federation, ORCID: https://orcid.org/0000-0001-9306-1280, e-mail: nchudova@gmail.com

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