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http://dx.doi.org/10.15207/JKCS.2020.11.9.021

Investigation of Research Trends in the D(Data)·N(Network)·A(A.I) Field Using the Dynamic Topic Model  

Wo, Chang Woo (Department of Computer Science, Chungbuk National University, SW&Cloud Planning Team, Institute of Information & Communications Technology Planning & Evaluation)
Lee, Jong Yun (Department of Computer Science, Chungbuk National University)
Publication Information
Journal of the Korea Convergence Society / v.11, no.9, 2020 , pp. 21-29 More about this Journal
Abstract
The Topic Modeling research, the methodology for deduction keyword within literature, has become active with the explosion of data from digital society transition. The research objective is to investigate research trends in D.N.A.(Data, Network, Artificial Intelligence) field using DTM(Dynamic Topic Model). DTM model was applied to the 1,519 of research projects with SW·A.I technology classifications among ICT(Information and Communication Technology) field projects between 6 years(2015~2020). As a result, technology keyword for D.N.A. field; Big data, Cloud, Artificial Intelligence, extended keyword; Unstructured, Edge Computing, Learning, Recognition was appeared every year, and accordingly that the above technology is being researched inclusively from other projects can be inferred. Finally, it is expected that the result from this paper become useful for future policy·R&D planning and corporation's technology·marketing strategy.
Keywords
Dynamic Topic Model; Research Trend Analysis; Time Series Analysis; Text Mining; Data Mining;
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1 T. K. Kang, Y. H. Kang, Y. C. Ryoo & T. S. Cheung. (2019). Research Trend in Ultra-Low Latency Networking for Fourth Industrial Revolution. Electronics and Telecommunications Trends, 34(6), 108-122. DOI : 10.22648/ETRI.2019.J.340610
2 J. Y. Lee & B. S. Cho. (2016). Suggestions for Nurturing Ecosystem to Spur Artificial Intelligence Industry. Electronics and Telecommunications Trends, 31(2), 51-62. DOI : 10.22648/ETRI.2016.J.310206
3 David M. Blei, Andrew Y. Ng. & Michael I. Jordan. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993-1022. DOI : 10.1162/jmlr.2003.3.4.-5.993
4 David M. Blei & John D. Lafferty. (2006). Dynamic Topic Models. Proceedings of the 23rd international conference on Machine learning, 113-120. DOI : 10.1145/1143844.1143859
5 Michal Rosen Zvi, Thomas Griffiths, Mark Steyvers & Padhraic Smyth. (2004). The Author-Topic Model for Authors and Documents. Proceedings of the 20th conference on Uncertainty in artificial intelligence, 487-494.
6 Pooja Kherwa & Poonam Bansal. (2019). Topic Modeling: A Comprehensive Review. EAI Endorsed Transactions on Scalable Information Systems, 7(24), 1-16. DOI : 10.4108/eai.13-7-2018.159623
7 Hamed Jelodar, Yongli Wang, Chi Yuan, Xia Feng, Xiahui Jiang, Yanchao Li & Liang Zhao. (2018). Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey. Multimedia Tools and Applications, 78, 15169-15211. DOI : 10.1007/s11042-018-6894-4
8 Wei Li & Andrew McCallum. (2006). Pachinko Allocation : DAG-Structured Mixture Models of Topic Correlations. Proceedings of the 23rd international conference on Machine learning, 577-584. DOI : 10.1145/1143844.1143917
9 Xuerui Wang & Andrew McCallum. (2006). Topics over time: a non-Markov continuous-time model of topical trends. Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, 424-433. DOI : 10.1145/1150402.1150450
10 H. J. Lee, P. M. Ryu, S. J. Lim, M. K. Jang & H. K. Ki. (2014). Technology Trends of AI for Big Data Knowledge Processing. Electronics and Telecommunications Trends, 29(4), 30-38. DOI : 10.22648/ETRI.2014.J.290404
11 S. I. Hwang & M. K. Kim. (2019). An Analysis of Artificial Intelligence(A.I.) : related Studies' Trends in Korea Focused on Topic Modeling and Semantic Network Analysis. Journal of Digital Contents Society, 20(9), 1847-1855. DOI : 10.9728/dcs.2019.20.9.1847   DOI
12 T. J. Kim. (2020). COVID-19 News Analysis Using News Big Data : Focusing on Topic Modeling Analysis. The Journal of the Korea Contents Association, 20(5), 457-466. DOI : 10.5392/JKCA.2020.20.05.457.   DOI
13 H. C. Lee, J. H. Jang & K. T. Kim. (2020). A Study on the Conflict Structure of the Standing Committee through Topic A[4nalysis of the National Assembly Minutes : Health and Welfare Committee in the First Half of the 20th National Assembly. Korean Party Studies Review, 19(2), 131-167. DOI : 10.30992/KPSR.2020.06.19.2.131   DOI
14 G. C. Kim & H. J. Noh. (2019). Research Trends of Regional Geography Education Using Topic Modeling. Social Studies Education, 58(4), 49-67. DOI : 10.37561/sse.2019.12.58.4.49   DOI
15 W. J. Lee & H. Y. Lee. (2019). A Technology Landscape of Artificial Intelligence: Technological Structure and Firms' Competitive Advantages. Journal of Korea technology innovation society, 22(3), 340-361. DOI : 10.35978/jktis.2019.06.22.3.340   DOI
16 K. W. Cho & Y. W. Woo. (2019). Topic Modeling on Research Trends of Industry 4.0 Using Text Mining. Journal of the Korea Institute of Information and Communication Engineering, 23(7), 764-770. DOI : 10.6109/jkiice.2019.23.7.764   DOI
17 Scott Deerwester, Susan T Dumais, George W Furnas, Thomas K Landauer & Richard Harshman. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6), 391-407. DOI : 10.1002/(SICI)1097-4571(199009)41:6<391   DOI
18 Thomas Hofmann. (2001). Unsupervised learning by probabilistic latent semantic analysis. Machine Learning, 42(1-2), 177-196. DOI : 10.1023/A:1007617005950   DOI
19 Lin Liu, Lin Tang, Wen Dong, Shaowen Yao & Wei Zhou. (2016). An Overview of Topic Modeling and its current applications in bioinformatics. Springerplus, 5(1), 1608-1630. DOI : 10.1186/s40064-016-3252-8   DOI
20 Arnab Bhadury, Jianfei Chen, Jun Zhu & Shixia Liu. (2016). Scaling up Dynamic Topic Models. WWW '16 : Proceedings of the 25th International Conference on World Wide Web, 381-390. DOI : 10.1145/2872427.2883046
21 Derek Greene & James P. Cross. (2017). Exploring the Political Agenda of the European Parliament Using a Dynamic Topic Modeling Approach. Political Analysis, 25(1), 77-94. DOI : 10.1017/pan.2016.7   DOI
22 Allen H. Huang, Reuven Lehavy, Amy Y. Zang & Rong Zheng. (2017). Analyst Information Discovery and Interpretation Roles: A Topic Modeling Approach. MANAGEMENT SCIENCE, 64(6), 2473-2972. DOI : 10.1287/mnsc.2017.2751   DOI
23 Andra-Selina Pietsch & Stefan Lessmannb. (2018). Topic modeling for analyzing open-ended survey responses, JOURNAL OF BUSINESS ANALYTICS, 1(2), 93-116. DOI : 10.1080/2573234X.2019.1590131   DOI
24 Paulo Bicalho, Marcelo Pita, Gabriel Pedrosa, Anisio Lacerda & Gisele L. Papp. (2017). A general framework to expand short text for topic modeling. Information Sciences, 393, 66-81. DOI : 10.1016/j.ins.2017.02.007   DOI