$\require{mediawiki-texvc}$

연합인증

연합인증 가입 기관의 연구자들은 소속기관의 인증정보(ID와 암호)를 이용해 다른 대학, 연구기관, 서비스 공급자의 다양한 온라인 자원과 연구 데이터를 이용할 수 있습니다.

이는 여행자가 자국에서 발행 받은 여권으로 세계 각국을 자유롭게 여행할 수 있는 것과 같습니다.

연합인증으로 이용이 가능한 서비스는 NTIS, DataON, Edison, Kafe, Webinar 등이 있습니다.

한번의 인증절차만으로 연합인증 가입 서비스에 추가 로그인 없이 이용이 가능합니다.

다만, 연합인증을 위해서는 최초 1회만 인증 절차가 필요합니다. (회원이 아닐 경우 회원 가입이 필요합니다.)

연합인증 절차는 다음과 같습니다.

최초이용시에는
ScienceON에 로그인 → 연합인증 서비스 접속 → 로그인 (본인 확인 또는 회원가입) → 서비스 이용

그 이후에는
ScienceON 로그인 → 연합인증 서비스 접속 → 서비스 이용

연합인증을 활용하시면 KISTI가 제공하는 다양한 서비스를 편리하게 이용하실 수 있습니다.

토픽모델링을 활용한 한국산업경영시스템학회지의 최근 연구주제 분석
Recent Research Trend Analysis for the Journal of Society of Korea Industrial and Systems Engineering Using Topic Modeling 원문보기

Journal of Korean Society of Industrial and Systems Engineering = 한국산업경영시스템학회지, v.46 no.3, 2023년, pp.170 - 185  

박동준 (부경대학교 통계.데이터사이언스 전공) ,  구평회 (부경대학교 시스템경영.안전공학부) ,  오형술 (강원대학교 AI소프트웨어학과) ,  윤 민 (부경대학교 응용수학과)

Abstract ▼ AI-Helper 아이콘AI-Helper

The advent of big data has brought about the need for analytics. Natural language processing (NLP), a field of big data, has received a lot of attention. Topic modeling among NLP is widely applied to identify key topics in various academic journals. The Korean Society of Industrial and Systems Engin...

주제어

#Topic Modeling   #BERTopic   #Latent Dirichlet Allocation   #Industrial Engineering  

참고문헌 (36)

  1. Angelov, D., Top2Vec: Distributed Representations of？Topics, https://arxiv.org/abs/2008.09470 

  2. BERTopic, https://maartengr.github.io/BERTopic/index.html. 

  3. Blei, D.M., Ng, A.Y., and Jordan, M.I., Latent Dirichlet？Allocation, Journal of Machine Learning Research, 2003,？3, pp. 993-1022. 

  4. Carnerud, D., 25 Years of Quality Management？Research-Outlines and Trends, International Journal of？Quality & Reliability Management, 2018, Vol. 35, No.？1, pp. 208-231. 

    상세보기

  5. 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. 

  6. 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. 

  7. C-TF-IDF, https://maartengr.github.io/BERTopic/getting_started/ctfidf/ctfidf.html. 

  8. 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. 

  9. Diaz-Papkovich, A., Anderson-Trocme, L., and Gravel,？S., A Reviw of UMAP in Population Genetics, Journal？of Human Genetics, 2021, Vol. 66, pp. 85-91. 

  10. Egger, R. and Yu, J., A Topic Modeling Comparison？Between LDA, NMF, Top2Vec, and BERTopic to？Demystify Twitter Posts, Frontiers in Socialogy, 2022,？May, Vol. 7, pp. 1-16. 

  11. Gaussier, E. and Coutte, C., Relation between PLSA？and NMF and Implications, Proceedings of the 28th？Annual International ACM SIGIR Conference on？Research and Development in Information Retrieval,？2005, August, pp. 601-602. 

  12. GENSIM Latent Dirichlet Allocation, https://radimrehurek.com/gensim/models/ldamodel.html 

  13. Hapke, H., Howard, C., and Lane, H., Natural Language？Processing in Action: Understanding, Analyzing, and？Generating Text with Python, 2019, Manning. 

  14. Hearst, M., What is Text Mining?, SIMA, https://www.jaist.ac.jp/~bao/MOT-Ishikawa/Furt-herReadingNo1.pdf. 

  15. Hofmann, T., Unsupervised Learning by Probabilistic？Latent Semantic Analysis, Machine Learning, 2001, 42,？pp. 177-196. 

  16. 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

  17. 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. 

  18. Jin, Y., Development of Word Cloud Generator Software？Based on Python, Science Direct, 2017, Vol. 174, pp.？788-792. 

  19. Jin, S.A., Heo, G.E., Jeong, Y.K., and Song, M.,？Topic-Network Based Topic Shift Detection on Twitter,？Journal of the Korean Society for Information Management, 2013, Vol. 3, pp. 285-302. 

  20. 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. 

  21. Kim, M.K., Lee, Y., and Han, C.H., Analysis of？Consulting Research Trends Using Topic Modeling,？Journal of Korean Society of Industrial and Systems？Engineering, 2017, Vol. 40, No. 4, pp. 46-54. 

  22. Ko, K.S. and Yang, J.K., Industrial Safety Risk Analysis？Using Spatial Analytics and Data Mining, Journal of？Korean Society of Industrial and Systems Engineering,？2017, Vol. 40, No. 4, pp. 46-54. 

  23. Kwon, S.H., Anomaly Detection of Big Time Series？Data Using Machine Learning, Journal of Korean Society？of Industrial and Systems Engineering, 2020, Vol. 43,？No. 2, pp. 33-38. 

  24. 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. 

  25. Langley, P., Selection of Relevant Features in Machine？Learning, AAAI Technical Report FS-94-02, 1994, pp.？127-131. 

  26. 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, pp.？100-108. 

  27. Park, C.E. and Lee, C.K., Sentimental Analysis of Korean？Movie Review Using Variational Inference and RNN？Based on BERT, The Korean Institute of Information？Scientists and Engineers Transactions on Computing？Practices, 2019, Vol. 25, No. 11, pp. 552-558. 

  28. Park, D.J., Oh, H.S., Kim, H.G., and Yoon, M., Topic？Modeling Analysis Comparision for Research Topic in？Korean Society of Industrial and Systems Engineering:？Concentrated on Research Papers from 1978- 1999,？Journal of Korean Society of Industrial and Systems？Engineering, 2021, Vol. 44, No. 4, pp. 113-127. 

  29. 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. 1, pp. 7-32. 

  30. pyLDAvis Documentation Release 2.2.2. August 24,？2018., https://buildmedia.readthedocs.org/media/pdf/py？ldavis/latest/pyldavis.pdf. 

  31. 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. 

  32. Ree, S.B., Analysis of Research Trends in Journal of？Korean Society for Quality Management by Text Mining？Processing, The Journal of Korean Society for Quality？Management, 2019, 47, pp 597-613. 

  33. Seo, H.B. and Lee, H.Y., PSS Research Trend, Proceeding of Spring Conference in the Korea Society for？Simulation, 2017, pp. 997-1017. 

  34. Syed, S. and Spruit, M., Full-Text or Abstract? Examining？Topic Coherence Scores Using Latent Dirichlet？Allocation, International Conference on Data Science？and Advanced Analytics, 2017, IEEE, pp. 165-174. 

  35. Teh, Y.W., Jordan, M., Beal, M.J., and Blei, D.M.,？Sharing Clusters Among Related Groups: Hierarchical？Dirichlet Processes, Journal of the American Statistical？Association, 2006, 101, pp. 1566-1581. 

  36. Vayansky, I. and Kumar, S.A.P., A Review of Topic？Modeling Methods, Information Systems, 2020, 94, pp.？1-15. 

저자의 다른 논문 :

관련 콘텐츠

오픈액세스(OA) 유형

FREE

Free Access. 출판사/학술단체 등이 허락한 무료 공개 사이트를 통해 자유로운 이용이 가능한 논문

섹션별 컨텐츠 바로가기

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

AI-Helper 아이콘
AI-Helper
안녕하세요, AI-Helper입니다. 좌측 "선택된 텍스트"에서 텍스트를 선택하여 요약, 번역, 용어설명을 실행하세요.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.

선택된 텍스트

맨위로