Open AccessTechnique for classifying the social network profiles according to the psychological scales based on machine learning methods
Author(s) -
Alexander Branitskiy,
Elena Doynikova,
Igor Kotenko
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1864/1/012121
Subject(s) - support vector machine , artificial intelligence , artificial neural network , computer science , convolutional neural network , machine learning , classifier (uml) , pattern recognition (psychology) , scope (computer science) , programming language
A technique for classifying the social network users and groups by psychological scales of the Ammon’s test has been developed. To analyze user profiles, we have used several types of artificial neural networks (support vector machine, linear regression, multilayer neural network and convolutional neural network), and for group analysis, we applied text classifiers (bag of words, weighted bag of words, continuous bag of words, skip-gram and fastText classifier). The scope of the technique is identifying deviations in the psychological state of users of social networks and monitoring these changes considering users’ groups to detect destructive influences. An experiment was carried out, as a result of which it was found that a multilayer neural network with an activation function of the ReLU type has the best accuracy.