Journal of Korean Society of Industrial and Systems Engineering (산업경영시스템학회지)

Society of Korea Industrial and System Engineering (한국산업경영시스템학회)
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Topic Modeling Analysis Comparison for Research Topic in Korean Society of Industrial and Systems Engineering: Concentrated on Research Papers from 1978~1999

한국산업경영시스템학회지 연구 주제의 토픽모델링 분석 비교: 1978년~99년 논문을 중심으로

  • Park, Dong Joon (Department of Statistics, Pukyong National University) ;
  • Oh, Hyung Sool (Department of Industrial and Management Engineering, Kangwon National University) ;
  • Kim, Ho Gyun (Department of Industrial and Management Engineering, Dongeui University) ;
  • Yoon, Min (Department of Applied Mathematics, Pukyong National University)
  • Received : 2021.11.14
  • Accepted : 2021.12.10
  • Published : 2021.12.31
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Abstract

Topic modeling has been receiving much attention in academic disciplines in recent years. Topic modeling is one of the applications in machine learning and natural language processing. It is a statistical modeling procedure to discover topics in the collection of documents. Recently, there have been many attempts to find out topics in diverse fields of academic research. Although the first Department of Industrial Engineering (I.E.) was established in Hanyang university in 1958, Korean Institute of Industrial Engineers (KIIE) which is truly the most academic society was first founded to contribute to research for I.E. and promote industrial techniques in 1974. Korean Society of Industrial and Systems Engineering (KSIE) was established four years later. However, the research topics for KSIE journal have not been deeply examined up until now. Using topic modeling algorithms, we cautiously aim to detect the research topics of KSIE journal for the first half of the society history, from 1978 to 1999. We made use of titles and abstracts in research papers to find out topics in KSIE journal by conducting four algorithms, LSA, HDP, LDA, and LDA Mallet. Topic analysis results obtained by the algorithms were compared. We tried to show the whole procedure of topic analysis in detail for further practical use in future. We employed visualization techniques by using analysis result obtained from LDA. As a result of thorough analysis of topic modeling, eight major research topics were discovered including Production/Logistics/Inventory, Reliability, Quality, Probability/Statistics, Management Engineering/Industry, Engineering Economy, Human Factor/Safety/Computer/Information Technology, and Heuristics/Optimization.

Keywords

Acknowledgement

This work was supported by a Research Grant of Pukyong National University(2021). We appreciate anonymous referees in commenting to improve the quality of our paper.

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