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논문검색

빅데이터를 활용한 유럽 지역연구에 대한 텍스트 마이닝과 네트워크 기법 및 토픽 모델링 분석

원문정보

A Big Data Analysis on Europe Area Studies using Text Mining, Network, and Topic Modeling

이재득

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초록

영어

The This study aims to analyze the papers on Europe published in Korea during the past 2000-2020 years. As the research methodologies, this study uses the text mining and Social Network Analysis such as frequency analysis, several centrality analyses, and topic analysis. After analyzing the empirical results, there has been a tendency to change the key words and centrality coefficients between 2000-2010 and 2011-2020 years. While the most frequent keywords were policy, system, culture, globalization, and state during 2000-2010 years, the main keywords were policy, integration, market, system, and labor during 2011-2020 years, The degree and closeness centrality analyses appeared the higher frequency key words. However, the eigenvector centrality appeared very different from the order of frequency key words. The topic analysis shows that the culture, migration, system, security, and democracy were the most important keywords during 2000-2010 years but policy, integration, economy, market, employment, and free trade agreement became the most important keywords duing 2011-2020 years in topic analysis.

목차

Abstract
Ⅰ. 서론
Ⅱ. 선행연구
Ⅲ. 연구 분석의 이론적 배경
Ⅳ. 실증분석
Ⅴ. 결론
참고문헌

저자정보

  • 이재득 Chae-Deug Yi. 부산대학교 교수

참고문헌

자료제공 : 네이버학술정보

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