<![CDATA[Understanding foreign policy trajectories across Southeast Asia requires tracking dynamic public diplomatic narratives. Existing computational models often prioritize prediction accuracy over qualitative interpretability. This study aims to establish an automated entity-based text-mining framework to track and visually map regional diplomatic themes across eleven Southeast Asian nations. Using a Python pipeline on a 2025 Google News corpus of 3,312 articles and 107,460 sentences, candidate topics were extracted via spaCy natural language processing, ranked through multi-factor composite scoring, mapped using NetworkX co-occurrence graphs, and grouped via K-Means clustering with Silhouette Analysis. The empirical findings reveal a clear structural hierarchy in media visibility, led by Singapore, Indonesia, and Malaysia. Bilateral connectivity peaks sharply around mainland border dynamics, particularly between Thailand and Cambodia. Semantic clustering demonstrates a dual-track narrative: localized economic integration and cross-border trade drive internal cooperation, whereas major-power competition, China’s economic footprint, and South China Sea maritime claims dominate external security discourse. This transparent, low-latency framework bridges computational text processing with qualitative policy analysis, offering a scalable tool for real-time diplomatic monitoring.]]>
Copyrights © 2026
