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Analyzing library and information science full‐text articles using a topic modeling approach
Author(s) -
Kurata Keiko,
Miyata Yosuke,
Ishita Emi,
Yamamoto Michimasa,
Yang Fang,
Iwase Azusa
Publication year - 2018
Publication title -
proceedings of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.193
H-Index - 14
ISSN - 2373-9231
DOI - 10.1002/pra2.2018.14505501143
Subject(s) - latent dirichlet allocation , topic model , computer science , information retrieval , data science , library science
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.