Proceedings of the 24th International Academic Conference, Barcelona

TOPIC DETECTION IN KOREAN SCM RESEARCH USING LATENT DIRICHLET ALLOCATION

MI-AE KIM, CHEA-YOUNG HWANG, CHANG-KYO SUH

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

DOI: 10.20472/IAC.2016.024.049

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