RADIOELECTRONIC AND COMPUTER SYSTEMS

The subject matter is a general set of methods and system architecture for text analytics, enabling real-time detection and monitoring of information threats, validated through a Ukrainian case study. It integrates sentiment analysis, polarity-inversion handling, and machine-learning–based thematic classification. The research is especially relevant in the context of hybrid warfare, where the information environment becomes a battlefield of disinformation, manipulative campaigns and cognitive influence. The goal is to develop and experimentally validate a comprehensive information technology system for automated threat detection in the Ukrainian information space, built on the principles of Responsible Artificial Intelligence (Responsible AI) and modern natural language processing techniques. The objectives: the formation of a multilingual corpus of news and social media texts; implementation of a sentiment analysis module that incorporates polarity inversion; development of a hybrid thematic classification method that combines keyword dictionaries with machine learning model ensembles; and the construction of a Responsible AI Evaluation (RAIE) framework with indicators for fairness, transparency and user satisfaction. The obtained results confirm all five proposed hypotheses: the developed sentime оьnt analysis module achieves macro-F1 = 0.85 and reduces MAE by 18.2% compared to the baseline model; the polarity inversion detection algorithm allows automatic reversal of sentiment score in manipulative texts, improving the detection of hostile narratives; the hybrid thematic classification achieves macro-F1=0.83, with latency of 55 ms/document and throughput of 18 documents/second; integration of all modules into a unified pipeline improves recall by 10.4% without significant increase in latency; the RAIE conceptual model ensures ΔF1 ≤ 5%, an expert user satisfaction score of 4.14/5 and less than 10% latency overhead. The conclusions demonstrate that the proposed system effectively combines high accuracy in identifying information threats with the principles of ethical AI, transparency and user trust, making it practically valuable for national cybersecurity centres, CERTs and OSINT platforms. Conclusions. The scientific novelty lies in the development of novel methods: a context-sensitive sentiment analysis approach tailored to military-related vocabulary; a polarity inversion algorithm for detecting covert hostility; a hybrid thematic classification model combining machine learning with expert dictionaries; an integrated information processing architecture with >17 documents/second throughput; a Responsible AI evaluation model incorporating Fairness Gap, Model Cards and User Satisfaction Score.

text mining; sentiment analysis; inversion detection; text classification; machine learning

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