Earticle

초록

영어

Accurate classification of public policy is essential for effective policy analysis, design, comparison, and formulation across countries. However, manual classification by policy experts can lead to inconsistencies and human errors, highlighting the need for a more reliable and efficient approach. This study proposes a deep learning-based model to support policy classification using artificial intelligence. Leveraging Korean policy datasets, comprising administrative data (1988–2018), legislative data (1987–2018), and media data (1988–2020), previously curated by experts, we developed an AI model for automated policy classification based on the KoBERT language model. Designed as a supplementary tool for policy experts, this model enhances classification consistency, reduces decision-making time, and improves overall productivity. Moreover, the model enables the classification, comparison, and evaluation of diverse policies at both local and national levels, offering valuable support for strategic public policy development. The proposed model achieved a Top-1 accuracy of 62.4% and a Top-3 accuracy of 71.6%, outperforming traditional baselines and demonstrating its practical potential for real-world policy analysis.

목차

ABSTRACT
Ⅰ. Introduction
Ⅱ. Literature Background
2.1. An Overview of Policy Classification Research
2.2. Deep Learning Models
Ⅲ. Research Methodology
3.1. Research context: Korean Comparative Agendas Project
3.2. Data Collection
Ⅳ. Model Design and Experimental Results
4.1. Database Construction
4.2. Additional Data Collection and Pre-processing
4.3. Policy Perceptron Model Design
4.4. Policy Perceptron Model Performance Verification
Ⅴ. Discussion and Conclusion
5.1. Theoretical Implications
5.2. Implications for Practice
5.3. Limitations of Study and Future Research

Appendix

키워드

저자

• Baek Jeong [ Lecturer, Department of Big Data Analytics, Kyung Hee University, Seoul, Korea ]
• Ha Eun Park [ Assistant Professor, Department of Big Data Analytics, Kyung Hee University, Seoul, Korea ] Corresponding Author
• Chae Won Lim [ Senior Researcher, Global Academy for Future Civilizations, Kyung Hee University, Seoul, Korea ]
• Kyoung Jun Lee [ AI & Business Professor, Kyung Hee University, Seoul, Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  Asia Pacific Journal of Information Systems

• 간기

  계간

• pISSN

  2288-5404

• eISSN

  2288-6818

• 수록기간

  1990~2026

• 등재여부

  KCI 등재,SCOPUS

• 십진분류

  KDC 325 DDC 658