Metrics for Personal Profiles of Social Network Users
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This paper discusses the technical details of obtaining and processing data to determine a set of characteristics of texts from social networks, genre preferences in movies and music genres for students of Kazan Federal University who have different academic performance (successful, average, not-successful). The selection of such characteristics is carried out using machine learning methods (Word2Vec, tSNE). The data obtained is used in the development of a functional psychometric model of cognitive behavioral predictors of an individual’s activity within the framework of their educational activities. We also developed a web application for visualizing the obtained data using the Flask engine.
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2020 ◽
Vol 34
(10)
◽
pp. 13971-13972
Keyword(s):
2022 ◽
Vol 2
(14)
◽
pp. 26-34
2020 ◽
Keyword(s):
2019 ◽
Vol 9
(2)
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pp. 2688-2693