Automating the Temperament Assessment of Online Social Network Users

Numerical data retrieved from the accounts of users of a popular Russian-language online social network have been used to automate the prediction of the PEN test (temperament test) results. This study aims to automate the assessment of personality traits of online social network users by comparing the test results and the content posted by the user on his or her account, using machine learning methods. Classifiers are constructed with CatBoost and random forest models for predicting the scores of extraversion-introversion and neuroticism. The theoretical significance of this result is the development of an approach to automating the assessment of human personality traits. The practical significance is the development of a program module to create an automated system for assessing the human personality traits through online social networks.