토픽모델링을 활용한 과학문화 이슈 분석:과학기술기본계획 시기별 뉴스 데이터를 중심으로

This study aims to explore science culture issues in Korea by analyzing news data from 2003 to 2022, covering the 1st to the 4th Science and Technology Master Plans. Using BIGKinds, a news big data analysis service, we collected 15,397 news articles by setting "Science Culture" as the search keyword and extracting relevant titles and full texts. To analyze the data, we applied Latent Dirichlet Allocation (LDA)-based topic modeling, a text mining technique, to identify underlying thematic structures. The analysis revealed changes in science culture issues over different periods, with major topics including ‘Science Popularization,’ ‘Industrial Innovation,’ ‘International Cooperation,’ and ‘Science Culture Infrastructure.’. As science and technology evolved to meet domestic and global demands, the emphasis on these topics shifted over time. ‘Science Popularization’ consistently emerged as a dominant theme, indicating sustained engagement with the public through education and outreach, and highlighting the mediating role of science in connecting with the economy, society, and arts. This study offers a systematic categorization and ranks of science culture trends and suggests policy directions for enhancing public participation and cross-sectoral integration.