텍스트 마이닝 기법을 활용한 학교문화예술교육 인식 탐색

Abstract

The purpose of this study was to examine the overall recognition of school arts and culture education and to use text mining techniques based on big data. As a keyword for data search, ‘school arts and culture education' was searched as a keyword, and the top 50 keyword were analyzed to compensate for the limitations of network simplification and network visualization centering on important nodes in the entire network. The data analysis period was conducted from April 10, 2015 to April 10, 2020 in the last 5 years, and the semantic connection network and N-gram network analysis were conducted in stages. The main analysis results of the study are as follows. First, looking at the top 10 words extracted based on the keyword of school arts and culture education, art instructor support projects (17,088 cases), arts and culture education (14,517 cases), schools (10,878 cases), culture (6,584 cases), arts (5,847), regional (2,117), programs (1,914), cartoon animation (1,756), research (1,716), and dance (1,602). Second, as a result of conducting N-gram network craft, arts and culture education / art lecturer support project (8,305 cases), culture / school (1,561 case), art / art lecturer support project (1,451 cases), culture arts education / activation (1,370 cases) ), school / arts and culture education (1,171 cases), dream attic / saturday culture school (1,079 cases), ability / ability development (1031 cases), elementary / school (948 cases), cartoon animation / arts and culture education (916 cases), and it was shown as the top 10 in the relationship diagram of culture / Korean traditional music (813 cases). It was possible to explore the implications of how the school arts and culture education is proceeding in a policy and institutional way.