Currently, the Internet information and communication network has become an integral part of human life. People use social networks such as Twitter, VKontakte, Facebook, etc., to establish global contacts, exchange opinions, gain knowledge, etc. The active participation of not only individual users, but also information organizations in the entire world space makes it necessary to develop measures that correspond to modern trends in the development of information and communication technologies to ensure national security, in particular, the organization of events related to countering the strengthening of ideas of extremism and terrorism. Countering the spread of aggressive information on the global network is an urgent problem of society and government agencies, this task is solved by filtering unwanted Internet resources. However, terrorist and extremist groups rationally use web technologies to perform various functions, including information dissemination, propaganda, fundraising and extremist missions. In such a situation, the Internet poses a threat to national security. In this paper, we investigate the issue of creating semantic analysis models to identify extremist messages in the Kazakh language. For the study, a proprietary text corpus was assembled and models based on bigrams and word input methods were proposed. According to the results of experiments, the proposed model shows the highest indicators for evaluating machine learning methods.