The Functions of Natural Language Processing in Evidence-based Policy Making

Document Type : Research Article

Author

Associate Professor of Science and Technology Policymaking, Iranian Research Institute for Information Science and Technology (IRANDOC), Tehran, Iran

10.22059/jppolicy.2025.102508

Abstract

With the increasing speed of complexity and scientific and technological advances, using artificial intelligence capabilities and especially natural language processing (NLP) to enhance evidence-based policymaking, ensuring more informed, responsive, and effective governance has become an urgent need. This study explores the roles and functions of Natural Language Processing (NLP) in supporting evidence-based policymaking in science and technology. Adopting a structured two-step research approach, the study first identifies and categorizes the key policy functions of NLP through a comprehensive literature rev-+iew and thematic analysis. Subsequently, it develops a conceptual framework using the Constant Comparative Method (CCM) to map these functions onto stages of the evidence-based policymaking process. The research reveals five primary NLP functions: contextual data analysis and information extraction, stakeholder feedback analysis, literature and expert opinion exploration, policy impact assessment, and public discourse analysis. These functions correspond to and enhance various stages of policymaking, from problem identification to policy evaluation. The resulting framework illustrates how NLP can augment human capabilities throughout the policy cycle, potentially leading to more data-driven, responsive, and transparent governance in science and technology domains.

Keywords

References

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Iranian Journal of Public Policy
Volume 11, Issue 2
June 2025
Pages 81-100

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