Fig. 1. Research process using topic modeling
Fig. 2. LDA(latent Dirichlet allocation) Model
Fig. 3. Frequent of factors
Fig. 4. Choosing optimal number of topics
Table 1. Summary of frequent factors
Table 2. Topic and documents number by topic modeling
Table 3. Factors of three topics by topic modeling
참고문헌
- J. H. Jo. (2012). A Study on the Causes Analysis and Preventive Measures by Disaster Types in Construction Fields. Journal of the Korea safety management & science, 14(1), 7-13. https://doi.org/10.12812/ksms.2012.14.1.007
- Ministry of Employment and Labor. (2017). Analysis of Industrial Accident Status.
- Korea Occupational Safety and Health Research Institute (KOSHA). (2014). Safety and Health Research Trends.
- Ministry of Employment and Labor. (2016). Korea Occupational Safety & Health Agency Evaluation Report for Prevention of Accident Prevention.
- S. W. Paik, H. J. Kim & D. H. Choi. (2012). A Study of Decreasing Critical Disastrous Accident in Small Construction Sites. Journal of the Korean Society of Agricultural Engineers, 54(6), 121-131. DOI: 10.5389/KSAE.2012.54.6.121
- M. Steyvers & T. Griffiths. (2007). Probabilistic topic models. Handbook of latent semantic analysis, 427(7), 424-440.
- Y. A. Hur, D. Y. Lee, K. K. Kim, W. H. Yu & H. S. Lim. (2017). A System for Automatic Classification of Traditional Culture Texts. Journal of the Korea Convergence Society, 8(12), 39-47. DOI: 10.15207/JKCS.2017.8.12.039
- R. Parimi & D. Caragea. (2011). Predicting friendship links in social networks using a topic modeling approach. In Pacific-Asia Conference on Knowledge Discovery and Data Mining. (pp. 75-86). Springer, Berlin, Heidelberg.
- P. DiMaggio, M. Nag & D. Blei. (2013). Exploiting affinities between topic modeling and the sociological perspective on culture: Application to newspaper coverage of US government arts funding. Poetics, 41(6), 570-606. https://doi.org/10.1016/j.poetic.2013.08.004
- W. Nie, X. Wang, Y. L. Zhao, Y. Gao, Y. Su & T. S. Chua. (2013). Venue semantics: Multimedia topic modeling of social media contents. In Pacific-Rim Conference on Multimedia. (pp. 574-585). Springer : Cham.
- J. H. Bae, N. G. Han & M. Song. (2014). Twitter issue tracking system by topic modeling techniques. Journal of intelligence and information systems, 20(2), 109-122. https://doi.org/10.13088/jiis.2014.20.2.109
- S. T. Na, J. H. Kim, M. H. Jung & J. E. Ahn. (2016). Trend Analysis using Topic Modeling for Simulation Studies. Journal of the Korea Society for Simulation, 25(3), 107-116. DOI: 10.9709/JKSS.2016.25.3.107
- S. G. Lee. (2018). A Study on the Trends of Construction Safety Accident in Unstructured Text using Topic Modeling. Journal of the Korea Academia-Industrial Cooperation Society, 19(10), 176-182. https://doi.org/10.5762/KAIS.2018.19.10.176
- Y. H. Kim & Y. S. Kim. (2019). Trend Analysis of Healthcare Research in Korea using Topic Modeling. Journal of Wellness, 14(1), 253-262. DOI: 10.21097/ksw.2019.02.14.1.253
- N. K. Jang & M. J. Kim. (2017). Research Trend Analysis in Fashion Design Studies in Korea using Topic Modeling. Journal of Digital Convergence, 15(6), 415-423. DOI:htts://doi.org/10.14400/JDC.2017.15.16.415
- J. Y. Yang. (2019). Convergence Study on Research Topics for Thyroid Cancer in Korea. Journal of the Korea Convergence Society, 10(2), 75-81. DOI: 10.15207/JKCS.2019.10.2.075
- D. M. Blei, A. Y. Ng, & M. I. Jordan. (2003). Latent Dirichlet Allocation. Journal of machine Learning research, 3, 993-1022.
- D. Mimno & A. McCallum. (2008). Topic Models Conditioned on Arbitrary Features with Dirichlet-Multinomial Regression. The 24th Conference on Uncertainly in Artificial Intelligence. (pp. 411-418).
- M. Hoffman, F. R. Bach & D. M. Blei (2010). Online learning for latent Dirichlet allocation. In advances in neural information processing systems. 856-864.
- D. Newman, J. H. Lau, K. Grieser & T. Baldwin. (2010). Automatic Evaluation of Topic Coherence. Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the ACL. (pp. 100-108).