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

研究成果: Contribution to journalArticle査読

3 被引用数 (Scopus)

抄録

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.

本文言語英語
ページ(範囲)847-848
ページ数2
ジャーナルProceedings of the Association for Information Science and Technology
55
1
DOI
出版ステータス出版済み - 1 2018

All Science Journal Classification (ASJC) codes

  • コンピュータ サイエンス(全般)
  • 図書館情報学

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