Abstract:
The second part of the work describes the most known tools for linguistic-statistical analysis of text corpuses and introduces RSA machine - a novel text analysis tool for socio-humanitarian research. This tool works with network representation of text and allows finding the constructions with complex graph structure in texts. RSA machine implements following features: search of constructions by query, computation of frequencies and statistical characteristics for search results, corpora or individual texts, comparing texts using statistical and frequency features. This paper describes the RSA machine architecture and developing tools. We present the results of pilot research of RSA machine using 142 texts examples written by people with different psychology and demographic characteristics. Some of them (18) were diagnosed with mental disorder. The performed correlation analysis revealed some relations between extracted texts attributes (e.g. frequency of predicate types) and results of psychological analysis performed by experts.
Citation:
J. M. Kuznetsova, I. V. Smirnov, M. A. Stankevich, N. V. Chudova, “Creating a text analysis tool for socio-humanitarian research. Part 2. RSA machine and the experience of using it”, Artificial Intelligence and Decision Making, 2019, no. 3, 40–51; Scientific and Technical Information Processing, 47:6 (2020), 374–382
\Bibitem{KuzSmiSta19}
\by J.~M.~Kuznetsova, I.~V.~Smirnov, M.~A.~Stankevich, N.~V.~Chudova
\paper Creating a text analysis tool for socio-humanitarian research. Part 2. RSA machine and the experience of using it
\jour Artificial Intelligence and Decision Making
\yr 2019
\issue 3
\pages 40--51
\mathnet{http://mi.mathnet.ru/iipr179}
\crossref{https://doi.org/10.14357/20718594190305}
\elib{https://elibrary.ru/item.asp?id=41216282}
\transl
\jour Scientific and Technical Information Processing
\yr 2020
\vol 47
\issue 6
\pages 374--382
\crossref{https://doi.org/10.3103/S0147688220060040}
This publication is cited in the following 6 articles:
D. K. Voronina, “Complex communicative and cognitive tasks in teaching a foreign language to students of information technology training areas”, jour, 29:1 (2024), 109
I. V. Smirnov, “Multilevel Natural Language Processing for Intelligent Information Retrieval and Text Mining”, Sci. Tech. Inf. Proc., 51:6 (2024), 629
Yu. M. Kuznetsova, A. A. Chuganskaya, N. V. Chudova, “Organization of Emotional Reactions Monitoring of Social Networks Users by Means of Automatic Text Analysis”, Sci. Tech. Inf. Proc., 51:6 (2024), 645
O. G. Grigoriev, D. A. Devyatkin, A. I. Molodchenkov, A. I. Panov, I. V. Smirnov, I. V. Sochenkov, N. V. Chudova, K. S. Yakovlev, “Artificial Intelligence and Cognitive Modeling: Creative Heritage of G.S. Osipov”, Sci. Tech. Inf. Proc., 51:6 (2024), 653
O. G. Grigoriev, A. A. Chuganskaya, M. A. Stankevich, “Identification of linguistic indicators of network socio-political discourse using text mining”, 50, no. 5, 2023, 414–421
Ivan Smirnov, Maksim Stankevich, Yulia Kuznetsova, Margarita Suvorova, Daniil Larionov, Elena Nikitina, Mikhail Savelov, Oleg Grigoriev, Lecture Notes in Computer Science, 12948, Artificial Intelligence, 2021, 232
Citation in format AMSBIB
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