This paper presents the results of automating the process of social network posts’ two-level hierarchical (ensemble) classification. The research aims to build a second-level model that allows to classify three main classes into subclasses using methods of multi-class classification (SVM, naive Bayesian classifier, random forest and multilayer perceptron). This work’s theoretical and practical significance is determined by the fact that the resulting model will partially automate the process of assessing the severity of users’ psychological characteristics on their text posts in social networks, as well as create the potential to refine the estimates of the protection of users from social engineering attacks, and the development of recommendation systems offering measures to improve users’ protection. The novelty of the work comes from the complementing of the previously developed two-level classification of social network posts with a second-level model based on known multiclass classification’s methods.