A Dynamic AI Framework for Monitoring and Forecasting Media Convergence Competitiveness

Abstract

In response to the limitations of traditional static and subjective methods, this study pioneers an AI-driven framework for assessing media convergence competitiveness by leveraging natural language processing and machine learning. Analyzing a large-scale dataset of YouTube comments from January to June 2024, the research employs sentiment classification models—including Support Vector Machine, Random Forest, and Gradient Boosting—with Gradient Boosting demonstrating superior performance. The findings reveal that the proposed framework not only outperforms conventional approaches in accuracy and consistency but also captures nuanced public sentiments and their temporal dynamics, uncovering a dual pattern of instant emotional reactions and reflective long-term evaluations. This provides policymakers and media practitioners with a scalable, data-driven tool for real-time assessment and evidence-based strategy formulation. Ultimately, the study underscores the transformative potential of integrating computational social science with media studies, significantly enhancing the precision and predictive power of competitiveness evaluation beyond qualitative paradigms.