There are many ways people express their reactions in the media. Text data is one of them, for example, comments, reviews, blog posts, messages, etc. Analysis of emotions expressed there is in high demand nowadays for various purposes. This research provides a method of performing sentiment analysis of text information using machine learning. The authors trained a classifier based on the BERT encoder, which recognizes emotions in text messages in English written in chat style. To handle raw chat-style messages, authors developed an enhanced text standardization layer. The list of emotions identified includes admiration, amusement, anger, annoyance, approval, caring, confusion, curiosity, desire, disappointment, disapproval, disgust, embarrassment, excitement, fear, gratitude, grief, joy, love, nervousness, optimism, pride, realization, relief, remorse, sadness, and surprise. The model solves the problem of multiclass multilabel text classification, which means that more than one class can be predicted from one piece of text. The authors trained the model on the GoEmotions dataset, which consists of 54,263 text comments from Reddit. The model reached a macro-averaged F1-Score of 0.50704 in emotions prediction and 0.7349 in sentiments prediction on the testing dataset. The presented model increased the quality of emotions prediction by 10.2% and sentiments prediction by 6.5% in comparison to the baseline approach.

The decision support subsystem in the neural network pattern recognition system, allowing to reduce the subjectivity and increase the quality of expert decisions in the construction of training samples by the statistical data of observations over the state of the management facilities of the production or social environment is developed. A functional structure of the NNPR system of the management facilities of the production or social environment by statistical information is proposed. According to the proposed structure, the problem-oriented NNPR system consists of subsystems of the initial data preparation for the PR and storage of the aggregated data, neural network models, and knowledge about the management facility of the production or social environment, as well as the support of the decision-making about its state. The decision support subsystem based on the proposed method of support of the classification decision-making using the Kohonen self-organizing layer is developed. The developed method of support of the classification decision-making provides a consistent implementation of the stages of self-organizing of neurons of the Kohonen computing layer, the calibration of the elements of the output vector of the training sample and their final labeling. An example of implementation of the proposed subsystem of support of decision-making about the class of the initialized labor protection project based on the expert estimates of the current level of the organization and working conditions at the enterprise is given. Using the developed decision support subsystem in the systems of NNPR by statistical information allows to adjust the decisions of the DM in accordance with the decisions of the neural network output machine, and thereby increase a relative share of correct expert estimates by 20 % on average and reduce false estimates for a number of pattern recognition problems by 50 %.

In order to obtain knowledge about the target audience, the preparation process of w e a k l y-structured multi-dimensional data of sociological surveys were automated. The following techniques have been developed for automating data prepa-ration: machine representation, preprocessing of the data from the sociological surveys in order to clean and filter it, transformation of data into feature space based on a formalized research objective, nonlinear dimensionality reduction and vis u a li -zation of the multi-dimensional data. As research has shown, the procedures associated with obtaining of primary and secondary feature spaces are the most significant. The Orange3 framework, which includes component-based data mining software and is used as a module for Python, were used to create IT of preparing weakly structured multidimensional data of sociological surveys in the Data Mining system. Approbation of the automated preparation process of weakly-structured multi- dimensional data within the sociological surveys Data Mining system allowed to increase the reliability of decision-making on the lifestyle of the respondents compared to a sociologist of the master's qualification level and the respondents own responses.

In this scientific publication, we suggest using the system of intellectual analysis of medical and sociological monitoring’s data using a neural network with a competitive Kohonen layer to automate the process of obtaining knowledge (metadata) about the state of public health of the target audience. The following specialized tools have been developed to implement the system: models and a method for presenting detailed and aggregated medical and sociological data in area of primary and secondary features; the method of neural network classification of respondents based on machine learning of a neural network with a competitive layer; the procedure for labeling neurons of the Kohonen layer, taking into account the classification decisions received from the sociologist-analyst (initial markers). At at the first step, a two-dimensional histogram of pairwise coincidences of neuron numbers and existing initial class markers was constructed,and thenit was corrected by lines and by columns in accordance with the developed rule. The result of the correctionis the correspondence matrix of the numbers of neurons of the Kohonen layer and existing markers of classification decisions. The testing of the developed models and methods is based on a system of intellectual analysis usingreal medical-sociological monitoring’s data.The research results show that it is possible to increase the relative share of correct classification decisions by an average of 20 % and reduce the share of false decisions by 50 % compared with the sociologist-analyst for tasks of intellectual analysis of medical and sociological monitoring’s data. These tasks were related to determining the working conditions of respondents.