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

doi:10.1002/pra2.2018.14505501143

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

Department of Library and Information Science

Research output: Contribution to journal › Article › peer-review

2 Citations (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 language	English
Pages (from-to)	847-848
Number of pages	2
Journal	Proceedings of the Association for Information Science and Technology
Volume	55
Issue number	1
DOIs	https://doi.org/10.1002/pra2.2018.14505501143
Publication status	Published - 2018 Jan 1

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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