빅데이터 분석기법을 활용한 치유농업 관련 조례 분석

Abstract

This study was conducted to analyze ordinances related to healing agriculture using text mining. As a method, data was collected using Python using data provided by the National Law Information Center, and 84 healing agriculture ordinances operated by local and basic organizations were analyzed. Four analysis techniques were utilized, and first, the word cloud analysis showed that 'commissioner' and 'committee' were most frequently mentioned. This indicates that commissioners who are experts in healing agriculture play an important role in the implementation of healing agriculture ordinances. Mention of local government leaders such as 'mayor, county governor' was high, confirming that they select and empower commissioners. Next, the N-gram analysis showed that 'foster -> support' had the highest connection strength. This indicates that support is being provided in various ways, including research and development related to healing agriculture, necessary facilities, and human resource training. In the topic modeling analysis, 'experts' was the highest. It is judged to be important to train experts so that healing agriculture can be continuously activated. The CONCOR analysis showed that 'committee' had the highest connection strength and clustering, and it was confirmed that members with expertise and experience in healing agriculture formed a committee to contribute to its revitalization. Finally, as a limitation of the study, it is difficult to interpret the specificity of healing agriculture because the analysis is likely to be based on the formal characteristics of legal documents. However, it is judged to be of great significance that the study was conducted in a situation where there is a lack of ordinance research using text mining.