This study maps the evolving landscape of Philippine political research using Natural Language Processing (NLP). Drawing from the Scopus academic database (n = 2,210 documents) as of May 10, 2025, we analyze scholarly outputs on Philippine politics to identify trends, thematic priorities, and keyword co-occurrence networks over time. We leverage author-defined keywords to uncover the explicit framing of political research. By doing so, we contribute to a deeper understanding of the intellectual architecture, emerging themes and topic networks shaping the study of politics in and of the Philippines. Using NLP techniques including keyword co-occurrence mapping and Latent Dirichlet Allocation (LDA) topic modeling, we trace the growth and diversification of research from 1988 to 2024. The results reveal a sharp increase in political scholarship in the past decade, with a notable surge after 2013. Philippine-based institutions such as the University of the Philippines System, Ateneo de Manila University, and De La Salle University dominate the production of knowledge, although foreign institutions such as the National University of Singapore and the Australian National University also make significant contributions. Thematic patterns suggest sustained scholarly interest in populism, Duterte-era politics, and regional geopolitics, alongside growing attention to indigenous issues, digital activism, and environmental governance. The topic modeling results uncover 20 latent topics that collectively reflect the intellectual direction of the field. This study demonstrates how computational methods can uncover hidden structures of political knowledge and inform future agendas in the study of Philippine politics.