원문정보
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
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