Algorithm for psycholinguistic analysis of social networks texts using the big five personality traits

The paper presents an approach to determining the psychological characteristics of a user of social networks through the analysis of text messages in social networks. The proposed approach includes the user's texts classification using machine learning. The results of the analysis of user surveys in accordance with the Big Five model, as well as a set of author's text data from social network pages, are used as training data. The questionnaire contains paired statements, and the respondent determines the degree of their own agreement with one or another statement on a scale from 0 to 4. Natural language text processing (NLP) methods were applied to the text resources used as input data for the classifier, as well as the RuWordNet linguistic ontology, in order to level out a number of features of social network texts, for example, the presence of grammatical errors and emoticons that complicate the process. semantic analysis. Two models were used as classifiers: the support vector machine and the random forest method. The area under the error curve (AUC ROC) metric was used to evaluate performance. The experiments used open text data of more than 1000 users of social networks.