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SNS Big-data Analysis and Implication of the Marine and Fisheries Sector

해양수산 SNS 빅데이터 분석 결과 및 시사점

  • Park, Kwangseo (Industry Intelligence and Strategy Research Division, Korea Maritime Institute) ;
  • Lee, Jeongmin (Industry Intelligence and Strategy Research Division, Korea Maritime Institute) ;
  • Lee, Sunryang (Industry Intelligence and Strategy Research Division, Korea Maritime Institute)
  • 박광서 (한국해양수산개발원 정책동향연구본부) ;
  • 이정민 (한국해양수산개발원 정책동향연구본부) ;
  • 이선량 (한국해양수산개발원 정책동향연구본부)
  • Received : 2017.04.26
  • Accepted : 2017.05.18
  • Published : 2017.05.25

Abstract

SNS Big-data Analysis means to find potential value from big data which has produced by the social media. In this paper, SNS Big-data has been analysed to find Korean concerns by using 24 key words from the marine and fisheries sector. Among 24 key words, seafood, shipping and Dokdo Island are the most mentioned ones. Some key words such as ocean policies and marine security that have less concerns have bess mentioned less. Also, key words that are led by government are mostly mentioned by news media, but key words that are led by private sector and have intimate relationship with people's lives are mostly mentioned by Blogs and Twitters. Therefore, reflecting close national concerns by SNS Big-data Analysis and especially resolving negative factors are the most significant part of the policy establishment. Also, differentiated promotion methods need to be prepared because the frequency of key words mentioned from each type of media are different.

SNS 빅데이터 분석은 소셜 미디어에서 생성되는 빅데이터로부터 숨겨진 가치를 찾아내는 것을 의미한다. 본고는 해양수산 분야의 국민적 관심사를 파악하기 위해 24개 키워드를 도출하여 SNS 빅데이터 분석을 실시하였다. 언급량이 많은 키워드는 수산물, 해운, 독도 순이었으며, 해양정책, 해양안보 등 국민적 관심사가 적은 키워드는 상대적으로 언급량이 미미했다. 매체별 언급량은 정부가 주도하는 분야는 뉴스에, 민간이 주도하거나 국민생활 연관성이 큰 경우는 블로그와 트위터에 많았다. 따라서 해양수산 정책 수립 시 SNS 빅데이터 분석을 활용해 국민적 관심사를 반영하고, 특히 부정적인 요인을 해소하는데 역점을 두어야 한다. 또한 매체별로 언급량이 다르므로 차별화된 홍보방안을 마련할 필요가 있다.

Keywords

References

  1. ETRI, Big data platform strategy, 2013.2.11.
  2. Ham, D.K., Yaleyong, and Woo,J.H., 2016.4, "A study on the Big-data based simulation for procument management of the shipyard fittings", Journal of the Korean Institute Of Industrial Engineers, 3142-3149.
  3. Kim, J.S, Oh, M.R, Cho, E.J, Kang, M.H. and Lee, E.J., 2016.10, "Climate Big-data fusion service in the fisheries sector", Korean Meteorological Society, 476-477.
  4. Kim, "U.K., 2014.12, Shipbuilding and offshore industry, and vessel navigation Big-data as a ICT fusion model", Journal of the KSME 54(12), 49-52.
  5. Lee, H.H., 2014, "Utilization of Big-data for reinforcing competitiveness of manufacturing", KIET Industrial Economics, 45-54.
  6. Lee, S.M., Yoon, S.H., Ha, S.W., Tumeejargal, Lee, H.H., Shin, H.S., Jeong, J.Y., Kim, C.S. and Yoon, D.G., 2014.6, "Sewol Sinking Disaster by Analysing Big Data", The Korean Society of Marine Environment & Safety, 90-92.
  7. Kim, S.H., Roh M.I., Kim, K.S. and Lee, S.M., 2016.1, "A Study on Big Data Platform Based on Hadoop for Ship and Offshore Industry", Journal of the Society for Computational Design and Engineering, 921-924.
  8. Tumenjargal Boldbaatar and Yoon, D.G., 2015.11, "A Study of Future Direction Policy in Arctic Transportation using Big Data", The Korean Society Of Marine Environment & Safety, 200-202.
  9. http://www.bloter.net/archives/137710.
  10. https://www.statista.com/statistics/254266/global-big-data-market-forecast/.