IDEAS home Printed from https://ideas.repec.org/p/sek/iacpro/4006385.html
   My bibliography  Save this paper

Topic Detection in Korean SCM Research Using Latent Dirichlet Allocation

Author

Listed:
  • Mi-Ae Kim

    (Kyungpook National University)

  • Chea-Young Hwang

    (Kyungpook National University)

  • Chang-Kyo Suh

    (Kyungpook National University)

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.

Suggested Citation

  • Mi-Ae Kim & Chea-Young Hwang & Chang-Kyo Suh, 2016. "Topic Detection in Korean SCM Research Using Latent Dirichlet Allocation," Proceedings of International Academic Conferences 4006385, International Institute of Social and Economic Sciences.
    • Handle: RePEc:sek:iacpro:4006385
    as

    Download full text from publisher

    File URL: https://iises.net/proceedings/24th-international-academic-conference-barcelona/table-of-content/detail?cid=40&iid=049&rid=6385
    File Function: First version, 2016
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Supply Chain Management; Latent Dirichlet Allocation; Topic Model;
    All these keywords.

    JEL classification:

    • M11 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Production Management
    • M10 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sek:iacpro:4006385. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Klara Cermakova (email available below). General contact details of provider: https://iises.net/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.