Metrics for Personal Profiles of Social Network Users

Abstract

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.