Metrics for Personal Profiles of Social Network Users

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 beetroot birkenstock 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 bostik universal primer pro 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.