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

SuYeon Kim (김수연); Song, Sung Jeon (송성전); Min Song (송민)

doi: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

Journal of the Korean Society for Information Management
Abbr : JKOSIM
2015, 32(1), pp.135~152
DOI : 10.3743/KOSIM.2015.32.1.135
Publisher : 한국정보관리학회
Research Area : Interdisciplinary Studies > Library and Information Science
Received : February 24, 2015
Accepted : March 16, 2015
Published : March 30, 2015

SuYeon Kim ¹, Song, Sung Jeon ², Min Song ¹

¹연세대학교
²연세대학교 문헌정보학과

Accredited

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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* References for papers published after 2023 are currently being built.

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Journal of the Korean Society for Information Management 2023 KCI Impact Factor : 1.31

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

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* References for papers published after 2023 are currently being built.

Journal of the Korean Society for Information Management 2023 KCI Impact Factor : 1.31

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

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* References for papers published after 2023 are currently being built.