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

1 Citation (Scopus)

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 - 2018 Jan 1

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Keywords

  • latent Dirichlet allocation
  • library and information science research
  • mapping
  • Topic modeling

ASJC Scopus subject areas

  • Computer Science(all)
  • Library and Information Sciences

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