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

박동준; 오형술; 김호균; 윤민

doi:10.11627/jksie.2021.44.4.113

[국내논문] 한국산업경영시스템학회지 연구 주제의 토픽모델링 분석 비교: 1978년~99년 논문을 중심으로
Topic Modeling Analysis Comparison for Research Topic in Korean Society of Industrial and Systems Engineering: Concentrated on Research Papers from 1978~1999 원문보기

Journal of Korean Society of Industrial and Systems Engineering = 한국산업경영시스템학회지, v.44 no.4, 2021년, pp.113 - 127

박동준 (부경대학교 통계학과) , 오형술 (강원대학교 산업경영공학과) , 김호균 (동의대학교 산업경영공학과) , 윤민 (부경대학교 응용수학과)

Abstract ▼ AI-Helper

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.

Keyword

참고문헌 (45)

Albalawi, Rania, Yeap, Tet. H., and Benyoucef, Morad., Using Topic Modeling Methods for Short-text Data: A Comparative Analysis, Frontiers in Artificial Intelligence, 2020, 3, pp. 1-14.

상세보기
Arun, R. Suresh, V., Mdahavan, C. E. V, and Murty, M. N., On Finding the Natural Number of Topics with Latent Dirichlet Allocations: Some Observation, PAKDD, Springer-Verlag, 2010, pp. 391-402.
Barde, B. V. and Bainwad, A. M., An Overview of Topic Modeling Methods and Tools, International Conference on Intelligent Computing and Control Systems, 2017, ICICCS, pp. 745-750.
Blei, D. M., Ng, A. Y., and Jordan, Michael, I., Latent Dirichlet Allocation, Journal of Machine Learning Research, 2003, Vol. 3, pp. 993-1022.
Chang, J., Boyd-Graber, J., Gerrish, S., Wang, C., and Blei, D. M., Reading Tea Leaves: How Humans Interpret Topic Models, In Advances in Neural Information Processing Systems, 2009, pp. 288-296.
Cho, G. H., Lim, S. Y., and Hur, S., An Analysis of the Research Methodologies and Techniques in the Industrial Engineering Using Text Mining, Journal of the Korean Institute of Industrial Engineers, 2014, Vol. 40, No. 1, pp. 52-59.

원문보기 상세보기
Cho, J. Y. and Cho, K. W., Topic Modeling on the Adolescent Problem Using Text Mining, Journal of the Korea Institute of Information and Communication Engineering, 2018, Vol. 22, No. 12, pp. 1589-1595.

원문보기 상세보기
Cho, S. G. and Kim, S. B., Finding Meaningful Pattern of Key Words in IIE Transactions Using Text Mining, Journal of the Korean Institute of Industrial Engineers, 2012, Vol. 38, No. 1, pp. 67-73.

원문보기 상세보기
Choi, J. W., Jang, J. J., Kim, D. H., and Yoon, J. H., Identifying Interdisciplinary Trends of Humanities, Sociology, Science and Technology Research in Korea Using Topic Modeling and Network Analysis, Journal of Society of Korea Industrial and Systems Engineering, 2019, Vol. 42, No. 1, pp. 74-86.
Chuang, J., Manning, C. D., and Heer, J, Termite: Visualization Techniques for Assessing Textual Topic Models, http://www.researchgate.net/publication/254004974.
Chung, K. S., Sin W. S., Baek, D. H., and Ju, Y. J., Review on the TQM Literature Appeared in KSQM, Journal of Korean Society for Quality Management, 2016, Vol. 44, No. 1, pp. 43-60.

원문보기 상세보기
Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., and Harshman, R., Indexing by Latent Semantic Analysis, Journal of the American Society for Information Science, 1990, Vol. 41, No. 6, pp. 391-407.

상세보기
Deisenroth, M. P., Faisal, A. A., and Ong, C. S., Mathematics for Machine Learning, Cambridge University Press, 2020.
Dinakar, K., Chen, J., Lieberman, H., Picard, R., and Filbin, R., Mixed Initiative Real-Time Topic Modeling & Visualization for Crisis Counseling, Proceedings of the 20th International Conference on Intelligent User Interfaces, 2015, pp. 417-426.
Hearst, M., What is Text Mining?, SIMA, https://www.jaist.ac.jp/~bao/MOT-Ishikawa/Furt-herReadingNo1.pdf.
Hofmann, T., Unsupervised Learning by Probabilistic Latent Semantic Analysis, Machine Learning, 2001, 42, pp. 177-196.
Hong, J. L., Yu, M. R., and Choi, B. R., An Analysis of Mobile Augmented Reality App Reviews Using Topic Modeling, Journal of Digital Contents Society, 2019, Vol. 20, No. 7, pp. 1417-1427.

상세보기
Jeong, B. K. and Lee, H. Y., Research Topics in Industrial Engineering 2001-2015, Journal of the Korean Institute of Industrial Engineers, 2016, Vol. 42, No. 6, pp. 421-431.

원문보기 상세보기
Jin, M. and Ko, H. K., Analysis of Trends in Mathematics Education Research Using Text Mining, Journal of Korean Society Mathematical Education Series E, 2019, Vol. 33, No. 3, pp. 275-294.
Kim, J. E. and Baek, S. G., Analysis of Issues on the College and University Structural Reform Evaluation Using Text Big Data Analytics, Asian Journal of Education, 2016, Vol. 17, No. 3 pp. 409-436.

상세보기
Kim, M. K., Lee, Y., and Han, C. H., Analysis of Consulting Research Trends Using Topic Modeling, Journal of Society of Korea Industrial and Systems Engineering, 2017, Vol. 40, No. 4, pp. 46-54.
Kim, S. K. and Jang, S. Y., A Study on the Research Trends in Domestic Industrial and Management Engineering Using Topic Modeling, Journal of the Korea Management Engineers Society, 2016, Vol. 21, No. 3, pp. 71-95.
Kim, S. Y., Analysis of Research Trends in SIAM Journal on Applied Mathematics Using Topic Modeling, Journal of the Korea Academia-Industrial Cooperation Society, 2020, Vol. 21, No. 7, pp. 607-615.

원문보기 상세보기
Korean Society of Industrial and Systems Engineering, http://www.ksie.or.kr.
Landauer, T. K., Foltz, P. W., and Laham, D., An Introduction to Latent Semantic Analysis, Discourse Processes, 1998, Vol. 25:2-3, pp. 259-284.

상세보기
Langley, P., Selection of Relevant Features in Machine Learning, AAAI Technical Report FS-94-02, 1994, pp. 127-131.
Lee, K. H., Jung, H. J., and Song, M., Weighted Subject - Method Network Analysis of Library and Information Science Studies, Journal of the Korean Society for Library and Information Science, 2015, Vol. 49, No. 3, pp. 457-488.

원문보기 상세보기
Lee, S. B., Analysis of Research Trends in Journal of Korean Society for Quality Management by Text Mining Processing, Journal of Korean Society for Quality Management, 2019, Vol. 47, No. 3, pp. 597-613.

원문보기 상세보기
Mashey, J., Big Dat and the Next Wave of Infrastress, https://static.usenix.org/event/usenix99/invited_talks/mashey.pdf.
McCallum, A. K., MALLET: A Machine Learning for Language Toolkit, 2002, http://mallet.cs.umass.edu/about.php.
Maier, D., Waldherr, A., Miltner, P., Wiedemann, G., Niekler, A., Keinert, A., Pfetsch, B., Heyer, G., Reber, U., Jaussler, T., Schmid-Petri, H., and Adam, S., Applying LDA Topic Modeling in Communication Research: Toward a Vaid and Reliable Methodology, Communication Methods and Measures, 2018, Vol. 12, No. 2-3, pp. 93-118.

상세보기
Mulunda, C. K., Wagacha, P. W., and Muchemi, L., Review of Trends in Topic Modeling Techniques, Tools, Inference Algorithms and Applications, The 5th International Conference on Soft Computing and Machine Intelligence, 2018, pp. 28-37.
Newman, D., Lau, J. H., Grieser, K., and Baldwin, T., Automatic Evaluation of Topic Coherence, Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the ACL, 2010, June, pp. 100-108.
Park, I. C., Kim, S. H., and Yoon, B. U., Technology Clustering Using Textual Information of Reference Titles in Scientific Paper, Journal of Society of Korea Industrial and Systems Engineering, 2020, Vol. 43, No. 2, pp. 25-32.
Park, J. H. and Song, M., A Study on the Research Trends in Library & Information Science in Korea using Topic Modeling, Journal of the Korean Society for Information Management, 2013, Vol. 30, No. 1, pp. 7-32.

원문보기 상세보기
Park, J. H. and Oh, H. J., Comparison of Topic Modeling Methods for Analyzing Research Trends of Archives Management in Korea: Focused on LDA and HDP, Korean Library And Information Science Society, 2017, Vol. 48, No. 4, pp. 235-258.
Park, S. U. and Lee, B. R., Trend Analysis of Korean Cultural Policy Studies Using Text Mining, The Korean Governance Review, 2017, Vol. 24, No. 3, pp. 95-119.

상세보기
Ramage, D., Rosen, E., Chuang, J., Manning, C. D., and McFarland, D. A., Topic Modeling for the Social Sciences, NIPS Workshop, 2009, pp. 1-4.
Rehurek, R. and Sojka, P., Software Framework for Topic Modelling with Large Corpora, The LREC Workshop on New Challenges for NLP Frameworks, 2010, pp. 45-50.
Seo, H. B. and Lee, H. Y., PSS Research Trend, Proceeding of Spring Conference in the Korea Society for Simulation, 2017, pp. 997-1017.
Siever, C. and Shirley, K. E., LDAvis: A Method for Visualizing and Interpreting Topics, Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces, 2014, pp. 63-70.
Teh, Y., Whye, J., Michaerl, B., Mattew. J., and Blei, D. M., Hierarchical Dirichlet Processes, Journal of the American Statistical Association, 2006, Vol. 101, pp. 1566-1581.

상세보기
Vayansky, I. and Kumar, S. A. P., A Review of Topic Modeling Methods, Information Systems, 2020, Vol. 94, pp. 1-15.
Yoon, S. Y. and Yoon, D. K., A Trend Analysis on Disaster and Safety Management Using Topic Modeling, Journal of the Korean Society for Geospatial Information Science, 2017, Vol. 25, No. 3, pp. 75-85.

상세보기
Zhao, W., Chen, J. J., Perkins, R., Liu, Z., Ge, W., Ding, Y., and Zou, W., A Heuristic Approach to Determine an Appropriate Number of Topics in Topic Modeling, BMC Bioinformatics, 2015, Vol. 16, No. S8, pp. 1-10.

저자의 다른 논문 :

활용도 분석정보

상세보기

다운로드

내보내기

활용도 Top5 논문

해당 논문의 주제분야에서 활용도가 높은 상위 5개 콘텐츠를 보여줍니다.
더보기 버튼을 클릭하시면 더 많은 관련자료를 살펴볼 수 있습니다.

표제어: PCR

동의어: Packet Collision Rate

용어 설명 출처 목록 (6)

용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

내보내기 구분	파일저장 인쇄 메일전송
구성항목	기본정보 상세정보 관리번호, 논문명, 저널/프로시딩명, 저자 , 발행년, 권, 호, 시작페이지, 끝페이지, 발행기관 관리번호, 논문명, 대등논문명, 저자 , 저널/프로시딩명, 발행기관, 발행년, 발행언어, 권, 호, 시작페이지, 끝페이지, ISBN, ISSN, 주제분야, 키워드, 초록(한글), 초록(영문), 저자(소속기관)
저장형식	Text(ASCII format) Excel format RefWorks Direct Export RIS format (for Reference Manager, ProCite, EndNote), Scholar's Aids, Mendeley
메일정보	받는사람 (필수) @ 보내는사람 (선택) @ 제목 내용 KISTI 검색결과 이메일 서비스
안내	총 건의 자료가 검색되었습니다. 다운받으실 자료의 인덱스를 입력하세요. (1-10,000) 검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다. 데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요) 다운로드 파일은 UTF-8 형태로 저장됩니다. 파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오. ~ Text(ASCII format) Excel format