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http://dx.doi.org/10.3743/KOSIM.2015.32.1.135

Investigation of Topic Trends in Computer and Information Science by Text Mining Techniques: From the Perspective of Conferences in DBLP  

Kim, Su Yeon (연세대학교)
Song, Sung Jeon (연세대학교 문헌정보학과 대학원)
Song, Min (연세대학교 문헌정보학과)
Publication Information
Journal of the Korean Society for information Management / v.32, no.1, 2015 , pp. 135-152 More about this Journal
Abstract
The goal of this paper is to explore the field of Computer and Information Science with the aid of text mining techniques by mining Computer and Information Science related conference data available in DBLP (Digital Bibliography & Library Project). Although studies based on bibliometric analysis are most prevalent in investigating dynamics of a research field, we attempt to understand dynamics of the field by utilizing Latent Dirichlet Allocation (LDA)-based multinomial topic modeling. For this study, we collect 236,170 documents from 353 conferences related to Computer and Information Science in DBLP. We aim to include conferences in the field of Computer and Information Science as broad as possible. We analyze topic modeling results along with datasets collected over the period of 2000 to 2011 including top authors per topic and top conferences per topic. We identify the following four different patterns in topic trends in the field of computer and information science during this period: growing (network related topics), shrinking (AI and data mining related topics), continuing (web, text mining information retrieval and database related topics), and fluctuating pattern (HCI, information system and multimedia system related topics).
Keywords
text mining; topic modeling; DMR; topic dynamics; research trend;
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