Journal of the Korea Academia-Industrial cooperation Society (한국산학기술학회논문지)

The Korea Academia-Industrial cooperation Society (한국산학기술학회)
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Analysis of Research Trends in SIAM Journal on Applied Mathematics Using Topic Modeling

토픽모델링을 활용한 SIAM Journal on Applied Mathematics의 연구 동향 분석

  • Kim, Sung-Yeun (Graduate School of Education, Incheon National University)
  • 김성연 (인천대학교 교육대학원)
  • Received : 2020.04.08
  • Accepted : 2020.07.03
  • Published : 2020.07.31
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Abstract

The purpose of this study was to analyze the research status and trends related to the industrial mathematics based on text mining techniques with a sample of 4910 papers collected in the SIAM Journal on Applied Mathematics from 1970 to 2019. The R program was used to collect titles, abstracts, and key words from the papers and to analyze topic modeling techniques based on LDA algorithm. As a result of the coherence score on the collected papers, 20 topics were determined optimally using the Gibbs sampling methods. The main results were as follows. First, studies on industrial mathematics were conducted in a variety of mathematics fields, including computational mathematics, geometry, mathematical modeling, topology, discrete mathematics, probability and statistics, with a focus on analysis and algebra. Second, 5 hot topics (mathematical biology, nonlinear partial differential equation, discrete mathematics, statistics, topology) and 1 cold topic (probability theory) were found based on time series regression analysis. Third, among the fields that were not reflected in the 2015 revised mathematics curriculum, numeral system, matrix, vector in space, and complex numbers were extracted as the contents to be covered in the high school mathematical curriculum. Finally, this study suggested strategies to activate industrial mathematics in Korea, described the study limitations, and proposed directions for future research.

본 연구는 텍스트 마이닝 기법을 이용하여 산업수학과 관련한 논문들의 연구 현황 및 동향을 파악하는데 목적이 있다. 이를 위해 R로 1970년부터 2019년까지 SIAM Journal on Applied Mathematics 총 4910편 논문의 제목, 초록, 주제어를 수집하였으며, LDA 알고리즘 기반의 토픽모델링 분석을 수행하였다. 수집된 자료에 대한 coherence score 분석 결과, 토픽의 최적 개수는 20개로 결정하였으며, 핵심 연구 주제들은 Gibbs 샘플링 방법을 기반으로 추출하였다. 주요 분석 결과는 다음과 같다. 첫째, 해석학과 대수학을 중심으로 계산수학, 기하학, 수학적 모델링, 위상수학, 이산수학, 확률 및 통계학 등 다양한 수학 분야에서 산업수학 관련 연구가 진행되었다. 둘째, 연대별 연구 주제의 동향을 분석한 결과, 상승하는 연구 주제는 수리생물학, 비선형편미분방정식, 이산수학, 통계학, 위상수학으로, 하강하는 연구 주제는 확률론만 나타났다. 셋째, 2015개정 수학교육과정에서 반영되지 않은 분야 중 고등학교 수학교육과정에서 다루어야 할 내용으로 기수법, 행렬, 공간벡터, 복소수가 도출되었다. 마지막으로 분석 결과를 바탕으로 우리나라의 산업수학 활성화 방안과 본 연구의 제한점 및 후속 연구를 제시하였다.

Keywords

References

  1. H. S. Lee, J. K. Park, O. J. Jung, AI mathematics is a global craze, but it's been deleted from high school curriculum in Korea [Internet], Hankyung, [Cited 2019 Oct. 06] Available From: https://news.naver.com/main/ranking/read.nhn?mid=etc&sid1=111&rankingType=popular_day&oid=015&aid=0004219450&date=20191006&type=1&rankingSeq=2&rankingSectionId=105 (accessed Apr. 07, 2020).
  2. K. B. Park, Linkage between industry and academia for promoting industrial mathematics, Policy Research Report, Science and Technology Policy Institute, Korea, pp.1-122.
  3. Ministry of Future, Planning and Science, Mathematics needed in industrial fields, the government will raise it, Press release, Sejong: Ministry of Future, Planning and Science, 2016.
  4. T. H. Kim, Without mathematics, there is no AI. It is imperative to prepare a domestic industrial mathematics ecosystem [Internet], Chosunbiz, [Cited 2019 September 16] Available From: https://biz.chosun.com/site/data/html_dir/2019/09/16/2019091601712.html (accessed Apr. 07, 2020)
  5. M. H. Kim, A study on strategy for vitalizing of industiral and applied mathematics as a national policy, Sejong: Ministry of Future, Planning and Science, 2015. DOI:https://doi.org/10.15724/jslhd.2019.28.3.081
  6. S. J. Kim, J. M. Sung, "Activation plan for entertainment industry mathematics", Journal of Korea Entertainment Industry Association, Vol.11, No.2, pp.289-297, Feb. 2017. DOI:https://doi.org/10.21184/jkeia.2017.02.11.2.289
  7. M. S. Shin, K. W. Cho, "Analysis of topic modeling and trend of journal of speech-language & hearing disorders using text mining: (2002-2018)", Journal of Speech-Language & Hearing Disorders, Vol.28, No.3, pp.81-91, Jul. 2019. DOI:https://doi.org/10.15724/jslhd.2019.28.3.081
  8. J. W. Mohr, P. Bogdanov, "Introduction-topic models: What they are and why they matter", Poetics, Vol.48, No.6, pp.545-569, Dec. 2013. DOI:https://doi.org/10.1016/j.poetic.2013.10.001
  9. T. K. Kim, H. R. Choi, H. C. Lee, "A study on the research trends in Fintech using topic modeling", Journal of the Korean Academia-Industrial Cooperation Society, Vol.17, No.11, pp.670-681, Nov. 2016. DOI:http://dx.doi.org/10.5762/KAIS.2016.17.11.670
  10. H. J. Jung, "Research dynamics in innovation studies using text mining", Journal of Technology Innovation, Vol.24, No.4, pp.249-275, Nov. 2016. DOI:https://doi.org/10.14383/SIME.2016.24.4.249
  11. A. Amado, P. Cortez, P. Rita, S. Moro, "Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis", European Research on Management and Business Economics, Vol.24, No.1, pp.1-7, Jan. 2018. DOI:https://doi.org/10.1016/j.iedeen.2017.06.002
  12. H. J. Yoon, C. S. Kim, K. Y. Kwahk, An analysis of research trends on local tour studies: Using text mining techniques, Proceedings of the Korean Academic Society of Tourism and Leisure, Korea, pp.275-282, Dec. 2017.
  13. J. H. Yoo, E. C. Jeon, H. N. Kim, "Study of research trends in climate change using text analysis-focusing on journal of climate change research-", Journal of Climate Change Research, Vol.10, No.3, pp.161-172, Sep. 2019. DOI:https://doi.org/10.15531/ksccr.2019.10.3.161
  14. H. J. Yoon, J. H. Park, J. W. Yoon, "Introduction of topic modeling for extracting potential information from unstructured text data: Issue analysis on news article of dementia-related physical activity", Korean Journal of Sport Science, Vol.30, No.3, pp.501-512, Sep. 2019. DOI:https://doi.org/10.24985/kjss.2019.30.3.501
  15. K. W. Cho, S. K. Bae, Y. W. Woo, "Analysis on topic trends and topic modeling of KSHSM journal papers using text mining", The Korean Journal of Health Service Management, Vol.11, No.4, Nov. pp.213-224. DOI:http://dx.doi.org/10.12811/kshsm.2017.11.4.213
  16. S. G. Kim, S. Y. Jang, "A study on the research trends in domestic industrial and management engineering using topic modeling", Journal of the Korea Management Engineers Society, Vol.21, No.3, pp.71-95, Sep. 2016. DOI:https://doi.org/10.5762/KAIS.2016.17.11.670
  17. S. Syed, C. T. Weber, "Using machine learning to uncover latent research topics in fishery models", Reviews in Fisheries Science & Aquaculture, Vol.26, No.3, pp.319-336, Jan. 2018. https://doi.org/10.1080/23308249.2017.1416331
  18. C. S. Kim, S. J. Choi, K. Y, Kwahk, "Investigation of research trends in information systems domain using topic modeling and time series regression analysis", Journal of Digital Contents Society, Vol.18, No.6, pp.1143-1150, Oct. 2017. https://doi.org/10.9728/dcs.2017.18.6.1143
  19. J. H. Park, M. Song, "A study on the research trends in library & information science in korea using topic modeling", Journal of the Korean Society for Information Management, No. 30, No.1, pp.7-32, Mar. 2013. DOI:https://doi.org/10.3743/KOSIM.2013.30.1.007
  20. J. G. Ahn, S. D. Kim, H. W. Kim, "Exploring dynamics of information systems research trend using text mining approach", Information Systems Review, pp.73-96, Sep. 2016. DOI:https://doi.org/10.14329/isr.2016.18.3.073
  21. K. Lee, H. Jung, M. Song, "Subject-method topic network analysis in communication studies". Scientometrics, Vol.109, No.3, pp.1761-1787, Dec. 2016. DOI:https://doi.org/10.14329/isr.2016.18.3.073
  22. D. M. Blei, "Probabilistic topic models: Surveying a suite of algorithms that offer a solution to managing large document archives", Communications of the ACM, Vol.55, No.4, pp.77-84, Apr. 2012. DOI:https://doi.org/10.1007/s11192-016-2135-7
  23. D. M. Blei, "Probabilistic topic models", 2013 Topic Modeling Workshop at NIPS, 2013.
  24. J. H. Park, H. J. Oh, "Comparison of Topic Modeling Methods for Analyzing Research Trends of Archives Management in Korea: focused on LDA and HDP", Journal of Korean Library and Information Science Society, Vol.48, No.4, pp.235-258, Dec. 2017. DOI:https://doi.org/10.16981/kliss.48.4.201712.235
  25. J. Chang, S. Gerrish, C. Wang, J. L. Boyd-Graber, D. M. Blei, "Reading tea leaves: How humans interpret topic models", In Advances in neural information processing systems, pp.288-296, Jan. 2009.
  26. D. Newman, J. H. Lau, K. Grieser, T. Baldwin, "Automatic evaluation of topic coherence", In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, CA, USA, pp.100-108, 2010.
  27. T. Jones, W. Doane, M. T. Jones, Functions for text mining and topic modeling [Internet], TextmineR, [Cited 2019 Jan. 01] Available From: https://www.rtextminer.com, (accessed Apr. 07, 2020).
  28. K. Hornik, B. Grun, "topicmodels: An R package for fitting topic models", Journal of statistical software, Vol.40, No.13, pp.1-30, May. 2011.
  29. Y. W. Kim, S. E. Ko, Y. L, Kim, H. S. Park, S. W. Lee, J. W. Jang, D. S. Cho, A study on the contents of mathematics learning suitable for future students, Korea Foundation for the Advancement of Science and Creativity, Korea, pp.110-111.