Analyzing library and information science full-text articles using a topic modeling approach

Keiko Kurata, Yosuke Miyata, Emi Ishita, Michimasa Yamamoto, Fang Yang, Azusa Iwase

Research output: Contribution to journalArticle

Abstract

The topic modeling approach can indicate hidden relationships between articles in a particular academic discipline. This study aims to examine topics in library and information science (LIS) using the latent Dirichlet allocation method. From representative five journals, 1,648 full-text articles were analyzed. We labeled 30 identified topics based on the top 10 highly weighted terms for each topic, title, and body of articles. From the topic mapping, commonly used methods and shift of research issues in LIS were found.

Original languageEnglish
Pages (from-to)847-848
Number of pages2
JournalProceedings of the Association for Information Science and Technology
Volume55
Issue number1
DOIs
Publication statusPublished - Jan 1 2018

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Information science
information science

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Library and Information Sciences

Cite this

Analyzing library and information science full-text articles using a topic modeling approach. / Kurata, Keiko; Miyata, Yosuke; Ishita, Emi; Yamamoto, Michimasa; Yang, Fang; Iwase, Azusa.

In: Proceedings of the Association for Information Science and Technology, Vol. 55, No. 1, 01.01.2018, p. 847-848.

Research output: Contribution to journalArticle

Kurata, Keiko ; Miyata, Yosuke ; Ishita, Emi ; Yamamoto, Michimasa ; Yang, Fang ; Iwase, Azusa. / Analyzing library and information science full-text articles using a topic modeling approach. In: Proceedings of the Association for Information Science and Technology. 2018 ; Vol. 55, No. 1. pp. 847-848.
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