토픽 모형 및 사회연결망 분석을 이용한 한국데이터정보과학회지 영문초록 분석 = Analysis of English abstracts in Journal of the Korean Data & Information Science Society using topic models and social network analysis

다국어 초록 (Multilingual Abstract)

This article analyzes English abstracts of the articles published in Journal of the Korean Data & Information Science Society using text mining techniques. At first, term-document matrices are formed by various methods and then visualized by social network analysis. LDA (latent Dirichlet allocation) and CTM (correlated topic model) are also employed in order to extract topics from the abstracts. Performances of the topic models are compared via entropy for several numbers of topics and weighting methods to form term-document matrices.

참고문헌 (Reference)

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5 Blei, D. M., "Dynamic topic models" 113-120, 2006

6 Huang, J., "Correlated topic model details" Carnegie Mellon University 2006

7 정희춘, "Conditional bootstrap confidence intervals for classification error rate when a block of observations is missing" 한국데이터정보과학회 24 (24): 189-200, 2013

8 Blei, D. M., "A correlated topic model of science" 1 : 17-35, 2007