<![CDATA[Background: Natural Language Processing (NLP), a subfield of Artificial Intelligence (AI), has advanced rapidly and can now analyze large volumes of textual data to extract meaningful information. This is highly relevant for supporting evidence-based, responsive, and accountable public policy through efficient analysis of public opinion, policy documents, and academic literature. Objective: This study maps research trends in the application of NLP to public policy through a bibliometric analysis and Dynamic Topic Modeling (DTM) approach, identifying the current topic structure, the temporal evolution of research themes, and the topics that may inform future research agendas. Methods: Following the PRISMA 2020 protocol, publications from 2016–2025 were retrieved from the Scopus and Web of Science databases and screened through a multi-stage selection process. A total of 1,134 publications were included in the final bibliometric analysis. The resulting abstracts were analyzed using BERTopic-based DTM. The best-performing hyperparameter configuration, chosen after comparing several candidates, achieved coherence and topic diversity scores, as well as an outlier ratio, within generally acceptable ranges, yielding a set of primary topics plus a smaller outlier topic. Results: Publications grew from fewer than 70 per year before 2021 to 377 in 2025, led by China (317), the United States (181), and India (158). AI Governance & Public Services was the largest topic (27.69% of documents) but showed a slowing topic-probability trend, while Green Energy & Low-Carbon Transition, Water Resource Governance, and Climate Policy & Environmental Awareness emerged as the clearest hot topics. COVID-19-related topics declined after 2022, following a marked increase in NLP research on public health during the pandemic. Conclusion: NLP research in public policy is shifting from governance- and pandemic-related themes toward AI-driven public services and socio-environmental sustainability. Future public health policy research should prioritize post-COVID-19 recovery and healthcare resilience, while sustainability-linked topics are expected to continue expanding. Keywords: Natural Language Processing, Dynamic Topic Modeling, Public Policy, Government, BERTopic ]]>
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