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'블록체인 활용' 관련 빅데이터를 활용한 토픽 분석: 신문기사를 중심으로

Topic Analysis Using Big Data Related to 'Blockchain usage': Focused on Newspaper Articles

  • Kim, Sungae (Div. of Technology Educuation, Woonam Middle school) ;
  • Jun, Soojin (Dept. of Innovation and Convergence, Hoseo University)
  • 투고 : 2020.01.25
  • 심사 : 2020.02.20
  • 발행 : 2020.02.29

초록

이 연구에서는 블록체인 기술의 활용과 관련된 주요 토픽을 분석하기 위해 신문기사에 나타난 '블록체인 기술 활용' 빅데이터를 토픽 모델링기법을 적용하였다. 이를 위해 2013년부터 2019년까지, 21개의 신문사로부터 15,617건을 대상으로 토픽을 추출하고 주요 트렌트를 시기별로 구분하여 분석하였다. 분석결과 블록체인기술 활용과 관련된 기사는 2015년부터 기하급수적으로 증가하였으며 IT_과학 분야와 경제 분야에 집중되었다. 기간에 따라 차이는 있지만 암호화폐, 비트코인, 가상화폐와 관련된 키워드의 가중치가 높았다. 금융거래에 집중되었던 블록체인기술은 빅데이터, 사물인터넷, 인공지능으로 점차 확대되었다. 이에 따라 기업의 토픽 변화도 함께 이루어져 금융거래를 위한 은행에서 다양한 분야로 확대되면서 대기업과 글로벌기업으로 집중되었다. 이 연구를 통해 블록체인기술의 활용과 관련한 신문기사의 주요 토픽과 함께 이러한 토픽들이 어떠한 변화추이를 보이고 있는지에 대해 확인할 수 있었다.

To analyze the main topics related to the use of blockchain technology, the Topic Modeling Technique was applied to the 'Blockchain Technology Utilization' big data shown in newspaper articles. To this end, from 2013 to 2019, when newspaper articles on the use of blockchain technology first appeared, the topics were extracted from 21 newspapers and analyzed by time to 15,537 articles. As a result of the analysis, articles related to the utilization of blockchain technology have increased exponentially since 2015 and focused on IT_science and economics. Key words related to cryptocurrency, bitcoin and virtual currency were weighted high, although they differed depending on time. Blockchain technology, which had focused on financial transactions, gradually expanded to big data, Internet of Things and artificial intelligence. As a result, changes in corporate topics were also made together to expand into various fields at banks for financial transactions, focusing on large and global companies. The study showed how these topics were changing, along with the main topics in newspaper articles related to the use of blockchain technology.

키워드

참고문헌

  1. WEF, (2015). Deep Shift Technology Tipping Points and Societal Impact, Retrieved from http://www3.weforum.org/docs/WEF_GAC15_Technological_Tipping_Points_report_2015.pdf
  2. Y. Lee, & S. H. Woo. (2018). Research for the convergence of IoT and Blockchain, in Proceeding of the of the Korean Institute of Information and Communication Sciences Conference 2018, Daejoen, 507-509,
  3. Ministry of Science and ICT, and Korea Institute of Science and Technology Planning and Evaluation, (2018). The future of blockchain,
  4. S. Nakamoto, (2008). Bitcoin: a peer-to-peer electronic cash system. Retrieved from http://bitcoin.org/bitcoin.pdf
  5. Y. K. Yang, S. B. Cho, & S. H. Chun. (2019). A Study on the Utilization Status and Development Plan for Blockchain Technology: Focusing on Cryptocurreny Policies of Foreign Countries. The Journal of Business Education, 33(2), 47-70. DOI : 10.34274/krabe.2019.33.2.003
  6. S. Kim & S. J. Jeon. (2019). Analysis of Keywords 'Using Blockchain Technology' through Text Mining: Focused on Newspaper Articles. In Proceeding of Korean Institute of Information and Telecommunication Women's ICT Conference, 73-76.
  7. O. S. Yang & J. H. Han. (2019). The Main Topics of Applying Blockchain Technology to the Core Industries in Gangwon Economy : Big Data Machine Learning-based Topic Modeling & Network Analysis. The Journal of Professional Management, 22(4), 307-334
  8. H. I. Jo, J. W. Kim, & B. G. Lee. (2019). A Study on Research Trends of Blockchain Using LDA Topic Modeling : Focusing on United States, China, and South Korea. Journal of Digital Contents Society, 20(7), 1453-1460. https://doi.org/10.9728/dcs.2019.20.7.1453
  9. S. M. Lee & S. G. Hong. (2019). Analysis of Blockchain Trends Using Topic Modelling Technique. Korean Institute of Information and Telecommunication Women's ICT Conference, 44-47.
  10. S. Kim, H. Park. & J. Lee. (2018). Word2vec based Latent Semantic Analysis (W2V-LSA): New Topic Modeling Method for Trend Analysis on Blockchain Technology Research, In Proceedings of the Korean Institute of Industrial Engineers 2018. 2018(11), 2296-2304
  11. D. M. Blei. (2012). Probabilistic topic models, Communication of the ACM, 55(4), 77-84. https://doi.org/10.1145/2133806.2133826
  12. J. Park, E. Park. & D. Jo. (2015). Automated Text Analysis of North Korean New Year Addresses : 1946-2015. Korean Political Science Review, 49(2), 27-61. https://doi.org/10.18854/kpsr.2015.49.2.002
  13. M. R. Jin & H. K. Go. (2019). Analysis of trends in mathematics education research using text mining. Communications of Mathematical Education, 33(3), 275 - 294
  14. H. Kim & H. Rhee. (2016). Trend Analysis of Data Mining Research Using Topic Network Analysis, Journal of the Korea Society of Computer and Information, 21(5), 141-148. DOI : 10.9708/jksci.2016.21.5.141
  15. D. M. Blei, (2012). Probabilistic topic models, Communications of the ACM, 55(4), 77-84. https://doi.org/10.1145/2133806.2133826
  16. T. L. Griffiths & M. Steyvers, (2004). Finding scientific topics, Proceedings of the National academy of Sciences, 5228-5235.