A Tool for Collecting and Assessing Public Opinion on the Transport Industry from Open Sources

The study presents the development of a tool for intelligent analysis of passenger feedback on transport. Purpose: is to create an automated monitoring and semantic analysis system aimed at transforming unstructured user statements into structured data for managerial decision-making. Methods: modern information technologies were used, including natural language processing methods, machine learning, and integration of data from heterogeneous sources. Results: emphasize the effectiveness of the proposed text processing pipeline, which includes classification by transport mode and topic, sentiment analysis, and named entity recognition, for identifying key issues and public opinion trends. Practical significance: lies in increasing the responsiveness of transport regulators to passenger problems, optimizing service, and transitioning to data-driven management. The research is important for the development of digital technologies in the transport industry and improving the efficiency of passenger transportation management in the context of digital transformation.