(Igor Sikorsky Kyiv Polytechnic Institute, 2023) Lande, Dmytro; Feher, Anatolii; Strashnoy, Leonard
The paper describes a methodology for forming thematic causal networks using artificial intelligence and automating the processes of their visualization. The presented methodology is considered on the example of ChatGPT, as an artificial intelligence for analyzing the space of texts and building concepts of causal relationships, and their further visualization is demonstrated on the example of Gephi and CSV2Graph programs. The effectiveness of the disaggregated method in relation to traditional methods for solving such problems is shown by integrating the means of intelligent text analytics and graphical network analysis on the example of the problem of data leakage in information systems and a selection of news clippings on the selected cybersecurity topic.