The object of the research are methods and algorithms of optimizing of the Big Data transformation to build a social profile model, the subject of the research are methods of constructing of a social profile. For decision-making person, the problem of scientific methodological and instrumental re-equipment is relevant for the effective fulfillment of a set of managerial tasks and confronting of fundamentally new challenges and threats in society. This task is directly related to the problem of building of a model of the social profile of both the individual and the social group as a whole. Therefore, the problem of optimizing of methods of constructing of a mathematical model of a social profile is certainly relevant. During the research, methods of the mathematical apparatus of graph theory, database theory and the concept of non-relational data stores, Big Data technology, text analytics technologies, parallel data processing methods, methods of neural networks' using, methods of multimedia data analyzing were used. These methods were integrated into the general method, called the method of increasing of the efficiency of constructing of a mathematical model of a social profile. The proposed method improves the adequacy of the social profile model, which will significantly improve and simplify the functioning of information systems for decision-making based on knowledge of the social advantages of certain social groups, which will allow dynamic correction of their behavior. The obtained results of testing the method make it possible to consider it as an effective tool for obtaining of an objective information model of a social portrait of a social group. This is because the correctness of setting and solving of the problem ensured that adequate results were obtained. Unlike the existing ones, the proposed modeling method, which uses an oriented graph, allows to improve significantly the quality and adequacy of this process. Further research should be directed towards the implementation of proposed theoretical developments in real decision-making systems. This will increase the weight of automated decision-making systems for social climate analysis.