Asia Pacific Journal of Information Systems (APJIS)

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Current Issue

Date	September 2025
Vol. No.	Vol. 35 No. 3
DOI	https://doi.org/10.14329/apjis.2025.35.3.650
Page	650~680
Title	Development of a Deep Learning-Based AI Model for Automating National Public Policy Classification
Author	Baek Jeong, Ha Eun Park, Chae Won Lim, Kyoung Jun Lee
Keyword	Policy Classification, Deep Learning, Artificial Intelligence, KoBERT, Comparative Agendas Project, Text Classification, Perceptron, Natural Language Processing, AI Model, Automation
Abstract	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.

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