Automatic Evaluation System for News Communication Effectiveness Based on Topic Features and Deep Learning

In the era of digital transformation, assessing the effectiveness of news communication has become a complex yet essential task. Traditional evaluation methods — centered on metrics such as reach, sentiment polarity, or basic user engagement — fail to fully capture the multifaceted dynamics of how audiences perceive, interpret, and respond to media content. These static approaches often disregard key variables such as audience heterogeneity, contextual variability, and the non-linear feedback mechanisms that characterize modern communication environments. To overcome these challenges, this study proposes a novel, intelligent evaluation framework that integrates formal symbolic modeling, deep semantic encoding, and adaptive strategy optimization. Central to this framework is the NOVA (News Outcome and Value Assessment) model, which leverages probabilistic graphical modeling in conjunction with deep learning-based semantic feature extraction to evaluate the cognitive, emotional, and behavioral dimensions of audience response. In addition, we introduce LUMIN (Learning and Understanding Mechanism for Influence Navigation), an adaptive influence strategy that combines reinforcement learning with context-aware optimization to dynamically refine communication tactics. Together, NOVA and LUMIN provide a comprehensive mechanism for both analyzing and enhancing news dissemination effectiveness. Experimental evaluations across varied communication contexts demonstrate that our integrated approach significantly outperforms traditional baselines in terms of adaptability, predictive accuracy, and robustness to environmental shifts. The proposed framework offers a forward-looking solution that aligns with the growing need for intelligent systems capable of navigating the complexity of digital media ecosystems. It supports a deeper understanding of media influence while enabling strategic improvements in communication efficacy.