Topic Detection in Korean SCM Research Using Latent Dirichlet Allocation

Mi-Ae Kim (), Chea-Young Hwang () and Chang-Kyo Suh ()
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Mi-Ae Kim: Kyungpook National University
Chea-Young Hwang: Kyungpook National University
Chang-Kyo Suh: Kyungpook National University

No 4006385, Proceedings of International Academic Conferences from International Institute of Social and Economic Sciences

Abstract: The supply chain management(SCM) is a cross-disciplinary research field and challenges to research SCM are increasing due to the rapid development of information system. We investigate the SCM research papers using latent Dirichlet allocation(LDA) to detect common and/or hidden topics and trends among Korean researcher in SCM. Topic modeling analyzes the words of the original texts to discover the topics and the LDA groups articles in several relevant topics and finds the hidden topics in the literature. In this research we searched RISS(www.riss.kr) and NDSL(www.ndsl.or.kr) database using keywords and collected academic papers on SCM between 2010 and 2014. Among them we analyze the abstract of the papers that were published by domestic authors to identify the topic trend in the field of SCM. The major findings will be discussed in the conference in details.

Keywords: Supply Chain Management; Latent Dirichlet Allocation; Topic Model (search for similar items in EconPapers)
JEL-codes: M10 M11 (search for similar items in EconPapers)
Pages: 1 page
Date: 2016-08
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Published in Proceedings of the Proceedings of the 24th International Academic Conference, Barcelona, Aug 2016, pages 201-201

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