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효율적인 트윗 분석 시스템 설계 및 구현 방법

An Efficient Method for Design and Implementation of Tweet Analysis System

  • 최민석 (삼육대학교 경영정보학과)
  • Choi, Minseok (Dept. of Management Information Systems, Sahmyook University)
  • 투고 : 2014.11.06
  • 심사 : 2015.02.20
  • 발행 : 2015.02.28

초록

다양한 소셜 네트워크 서비스의 등장과 사용자의 급증으로 소셜 네트워크 상에서 생산되는 데이터가 급증하고 있다. 전파 속도가 빠르고 개인적 성향의 의견들을 많이 포함하고 있는 소셜 네트워크 데이터의 특성으로 이를 분석하여 다양한 방면으로 활용하려는 요구도 증가하고 있다. 이러한 요구에 부응하여 실시간으로 대용량 데이터를 분석 처리하기 위한 다양한 기술 및 서비스들이 등장하고 있지만, 단기간에 적은 비용으로 그것들을 적용하기에는 어려움이 따른다. 본 논문에서는 새로운 기술이나 서비스의 도입 없이 효과적으로 트윗을 분석하기 위한 시스템 설계 및 구현 방법을 제안한다. 리눅스 기반의 호스팅 서버에 MySQL 데이터베이스와 PHP 스크립트를 이용하여 트윗 데이터를 수집하고 분석하는 모니터링 시스템을 구축하여 제안된 방법을 검증하였다.

Since the popularity of social network services (SNS) rise, the data produced from them is rapidly increased. The SNS data includes personal propensity or interest and propagates rapidly so there are many requests on analyzing the data for applying the analytic results to various fields. New technologies and services for processing and analyzing big data in the real-time are introduced but it is hard to apply them in a short time and low coast. In this paper, an efficient method to build a tweet analysis system without inducing new technologies or service platforms for handling big data is proposed. The proposed method was verified through building a prototype monitoring system to collect and analyze tweets using the MySQL database and the PHP scripts.

키워드

참고문헌

  1. http://www.twitter.com
  2. http://www.facebook.com
  3. https://blog.twitter.com/2010/measuring-tweets
  4. https://blog.twitter.com/2013/new-tweets-per-second-record-and-how
  5. Special Report : The next Google, Nature, Vol.455, Sep. 2008.
  6. http://hadoop.apache.org
  7. Song Ji Hoon, Lee Si Jin, Park Hyo Dong, Twitter message analysis system design using Hadoop, Proceedings of Korean society for internet information 2012, Vol. 13, No. 1, pp.169-171, 2012.
  8. Hyeokju Lee, Myoungjin Kim, Hanku Lee, Hyogun Yoon, Design and Implementation of an Analysis module based on MapReduce for Large-scalable Social Data, Proceedings of Korea Computer Congress 2011, Vol.38, No.1(B), pp.357-360, 2011.
  9. Jong-Soo Sohn, Soo-Whan Cho, Kyung-Lag Kwon, In-Jeong Chung, Improved Social Network Analysis Method in SNS, Journal of Intelligent Information System, Vol. 18, No. 4, pp.117-127, 2012.
  10. Yun-Mo Koo, Jeongjin Lee, Jinwook Seo, A Visual Analytics for Analyzing Social Networking Patterns among Microbloggers, Journal of Korea Game Society, Vol. 12, No. 3, pp.77-86, 2012. https://doi.org/10.7583/JKGS.2012.12.3.77
  11. Byoung-Yup Lee, Jong-Tae Lim, Jaesoo Yoo, Utilization of Social Media Analysis using Big Data, Journal of the Korea Contents Association, Vol. 13 No.2, pp 211-219, 2013. https://doi.org/10.5392/JKCA.2013.13.02.211
  12. Jeong Heo, Pum-Mo Ryu, Yoon Jae Choi, Hyun Ki Kim, Cheol Young Ock, An Issue Event Search System based on Big Data for Decision Supporting: SocialWisdom, Journal of KIISE, Vol. 40, No. 7, pp. 381-394, 2013.
  13. Byong-Kook Yoo, Soon-Hong Kim, Marketing Strategies using Social Network Analysis: Twitter's Search Network, Journal of the Korea Contents Association, Vol. 13 No. 5, pp. 396-407, 2013. https://doi.org/10.5392/JKCA.2013.13.05.396
  14. Seol A Jin, Go Eun Heo, Yoo Kyung Jeong, Min Song, Topic-Network based Topic Shift Detection on Twitter, Journal of Korea Society for Information Management, Vol. 30, No. 1, pp. 285-302, 2013. https://doi.org/10.3743/KOSIM.2013.30.1.285
  15. http://www.socialmetrics.co.kr
  16. http://www.pulsek.com
  17. https://dev.twitter.com/rest/public/search