The purpose of research is to increase the level of specification of sentiment within the framework of sentiment analysis of Russian-language texts by developing a dataset with an extensive set of emotional categories. The paper discusses the main methods of sentimental analysis and the main emotional models. A software system for decentralizing data tagging has been developed and described. The novelty of this work lies in the fact that to determine the emotional coloring of Russian-language texts, an emotional model is used for the first time, which contains more than 8 emotional classes, namely the model of R. Plutchik. As a result, a new dataset was developed for the study and analysis of emotions. This dataset consists of 24,435 unique records labeled into 32 emotion classes, making it one of the most diverse and detailed datasets in the field. Using the resulting dataset, a neural network was trained that determines the author’s set of emotions when writing text. The resulting dataset provides an opportunity for further research in this area. One of the promising tasks is to enhance the efficiency of neural networks trained on this dataset.

Keywords: sentiment, analysis, model, Robert Plutchik, emotions, markup, text

The problem of human self-knowledge is very relevant nowadays. People are constantly looking for new methods to study their own “I”. Graphology is one of such methods. The main difficulty of graphological analysis is the lack of automation of the process, the result depends only on the knowledge of the person. In addition, at the moment there is no service capable of carrying out a competent analysis of a person's handwriting. However, there are computer vision methods that, in combination, can produce work similar to that of a graphologist. Such methods include segmentation, binarization, and integral estimation methods. To compare the characteristics of handwriting with human characteristics of character, it is necessary to use classifiers. The use of all methods makes it possible to create a service that automates graphological analysis.

Keywords: graphology, handwriting, personal characteristics, graphological analysis, slope, direction of handwriting, text