The approach to application of the model "information gatekeepers" for filtering messages, taking into account the tonality of text data and making a decision on further dissemination of information for the implementation of socially necessary restrictions on the processes of dissemination and obtaining information is considered in the paper. The peculiarities of the classical models of "information gatekeepers" are analyzed and the approach to clustering of messages in social networks is proposed based on the function of evaluation of the message using sentiment data analysis for the implementation of the procedures of "semantic content filtering". The specifics of the sources of information, the ways of its presentation, the peculiarities of the formation of target communities, which are information oriented, socio-political, religious, ethnic, cultural, legal, age, and other aspects of social life have been taken into account. The main actor of the system based on the use of this model is the "information gatekeeper", which, before the broadcast of the message via specific communication channel, implements an evaluation function for the message. The problem of sentimental analysis of custom text data is considered. An algorithm for classifying custom texts is introduced. The rules for creating a dictionary of basic words reflecting the evaluation of a particular object and methods for classifying texts are described. The algorithm of allocation of problem statements is proposed. To solve the problem of sentiment text analysis, an approach based on knowledge is used that involves the use of additional expert resources in the form of dictionaries of indicative words and expressions, composed manually or automatically, and writing rules that reflect the structure of fragments of text data. The advantage of this approach is the ability to ensure the effectiveness of the classification of texts without loss of quality of work for various subject areas.