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텍스트 마이닝을 활용한 연구 동향 분석

Analysis of Research Trends Using Text Mining

  • 심재권 (고려대학교 영재교육원)
  • 투고 : 2020.02.29
  • 심사 : 2020.04.28
  • 발행 : 2020.04.30

초록

본 논문은 융복합 논문지인 창의정보문화연구의 연구 동향을 분석하기 위한 목적으로 텍스트 마이닝 방법을 활용하였다. 기존의 연구동향 분석방법은 전통적인 내용분석 방법을 사용하여 연구자 개인의 성향이 반영되는 한계가 있었다. 따라서, 기존 연구 동향 분석의 한계를 보완하고자 본 논문에서는 토픽 모델링 기법을 사용하였고, 창의정보문화연구 논문지의 2015년에서 2019년까지 발간된 논문 전체의 영문초록을 분석하였다. 분석 결과, 가장 많이 등장한 단어는 "education"이었고, 8개의 연구 주제가 도출되었다. 도출된 주제는 교육대상, 교육평가, 학습자역량, 소프트웨어와 메이커 문화, 정보교육과 컴퓨터교육, 미래교육, 창의성, 교수학습방법으로 분석되었다. 본 논문의 텍스트 마이닝을 활용하여 융복합연구 논문지의 연구동향을 분석하였다는 점에서 의의가 있다고 할 수 있다.

This study used the text mining method to analyze the research trend of the Journal of Creative Information Culture(JCIC) which is the journal of convergence. The existing research trend analysis method has a limitation in that the researcher's personality is reflected using the traditional content analysis method. In order to complement the limitations of existing research trend analysis, this study used topic modeling. The English abstract of the paper was analyzed from 2015 to 2019 of the JCIC. As a result, the word that appeared most in the JCIC was "education," and eight research topics were drawn. The derived subjects were analyzed by educational subject, educational evaluation, learner's competence, software education and maker culture, information education and computer education, future education, creativity, teaching and learning methods. This study is meaningful in that it analyzes the research trend of the JCIC using text mining.

키워드

참고문헌

  1. K.H. Joo, "Analysis of Performance of Creative Education based on Twitter Big Data Analysis", Journal of Creative Information Culture, Vol.5, No.3, pp.215-223, 2019. https://doi.org/10.32823/JCIC.5.3.201912.215
  2. H.K. Shin, "Design and Implementation of Natural Language Processing System Based on Discourse Representation Theorem", Journal of Creative Information Culture, Vol.1, No.2, pp.101-107, 2015.
  3. H.I Jo, J.W Kim, and B.G Lee, "A Study on Research Trends of Blockchain Using LDA Topic Modeling : Focusing on United States, China, and South Korea", Journal of Digital Contents Society, Vol.20, No.7, pp.1453-1460, 2019. https://doi.org/10.9728/dcs.2019.20.7.1453
  4. J.W. Kim, D.J. Kim, "A Study on the Research Trends of Social Studies Using Text Mining: Focused on Academic Papers After 2000", Theory and Research in Citizenship Education, Vol.51. No.2, pp.35-70. https://doi.org/10.35557/trce.51.2.201906.002
  5. J.S. Park, S.G. Hong and J.W Kim, "A Study on Science Technology Trend and Prediction Using Topic Modeling", Journal of the Korea Industrial Information Systems Research, Vol.22, No.4, pp.19-28, 2017. https://doi.org/10.9723/JKSIIS.2017.22.4.019
  6. H.Y. Kim, D.H. Seo, "A Topic Modeling Approach to the Analysis of Domestic Performance", Official Journal of the Koeran Society of Dance Science, Vol.36, No.3, pp.67-78, 2019.
  7. D.M. Blei, "Probabilistic topic models", Communications of the ACM, Vo.55, No.4, pp.77-84, 2012. https://doi.org/10.1145/2133806.2133826
  8. C.H. Papadimitriou, P. Raghavan, H. Tamaki, and S. Vempala, "Latent semantic indexing: A probabilistic analysis", In Proceedings of the 17th ACM Symposium on the Principles of Database Systems, pp.159-168, 1998.
  9. T. Hofmann, "Probabilistic latent semantic indexing", In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, pp.50-57, 1999.
  10. D.M. Blei, A.Y. Ng, and M.I. Jordan, "Latent Dirichlet Allocation", Journal of Machine Learning Research, Vol.3, pp.993-1022, 2003.
  11. S.I. Shin, M.S. Ha, and J.K. Lee, "Rediscovering the Interest of Science Education: Focus on the Meaning and Value of Interest", Journal of the Korean Association for Research in Science Education, Vol.38, No.5, pp.705-720, 2018.
  12. A.H. Lee, "Domestic Research Trends Analysis of Software Education", The Journal of Educational Information and Media, Vol.24, No.2. pp.277-301, 2018.
  13. H.S. Cho, H.J. Joo, "A Comparative Review on Research Trends in STEAM Education for Early Childhood and Elementary by Using Keyword Network", Early Childhood Education Research & Review, Vol.23, No.5, pp.177-260, 2019.
  14. Y.H Noh, T.Y. Kim, D.K Jeong, and K.H. Lee, "Trend Analysis of Convergence Research based on Social Big Data", The Journal of the Korea Contents Association, Vol.19, No.2, pp.135-146, 2019. https://doi.org/10.5392/JKCA.2019.19.02.135
  15. H.S. Kim, "Exploring Information Ethics Issues based on Text Mining using Big Data from Web of Science", The Journal of Korean Association of Computer Education, Vol.22, No.3, pp.67-78. https://doi.org/10.32431/KACE.2019.22.3.006