DOI QR코드

DOI QR Code

Extracting Method of User's Interests by Using SNS Follower's Relationship and Sequential Pattern Evaluation Indices for Keyword

키워드를 위한 시퀀셜 패턴 평가 지표와 SNS 팔로워의 관계를 이용한 사용자 관심사항 추출방법

  • Shin, Bong-Hi (Dept. of Computer Science & Engineering, Incheon National University) ;
  • Jeon, Hye-Kyoung (Dept. of Computer Science & Information Technology, Inha University)
  • 신봉희 (인천대학교 컴퓨터공학부) ;
  • 전혜경 (인하대학교 컴퓨터공학부)
  • Received : 2017.07.10
  • Accepted : 2017.08.20
  • Published : 2017.08.28

Abstract

Due to the spread of SNS, web-based consumer-generated data is increasing exponentially. It is important in many fields to accurately extract what is appropriate for the user's interest in a large amount of data. It is especially important for business mangers to establish marketing policies to find the right customers for them in many users. In this paper, we try to obtain important information centering on customers who are interested in each account through Twitter follow - following relationship. Because Twitter's current follower relationships do not reflect the user's interests, we try to figure out the details of interest using keyword extraction methods for tweets of followers. To do this, we select two domestic commercial Twitter accounts and apply the sequential pattern evaluation index to the mining key phrase of the text data collected from the follower.

SNS 등의 보급으로 인해 Web 기반의 소비자 생성 데이터는 기하급수적으로 늘어나는 추세이다. 수많은 데이터 속에서 사용자의 관심에 맞는 콘텐츠를 정확히 추출하는 것은 여러 분야에서 중요하다. 특히 비즈니스 분야에서는 많은 사용자들 속에서 자신들에게 적합한 고객을 찾아 마케팅 정책을 수립하는 것이 중요하다. 본 논문에서는 트위터의 팔로우-팔로잉 관계를 통해 각 계정에 관심이 있는 고객들을 중심으로 중요한 정보를 얻고자 한다. 현재 트위터의 팔로워 간의 관계는 사용자의 세부 관심 사항을 반영하지 않는다. 그러므로 본 연구에서는 팔로우들의 트윗에 대한 키워드 추출 방법을 사용하여 세부 관심 사항을 파악하려고 한다. 이를 위해 국내 상업 트위터 계정 2곳을 선정하여 팔로워로부터 수집한 텍스트 데이터의 마이닝 핵심 문구에 대한 순차 패턴 평가 지표를 적용한다.

Keywords

References

  1. M. S. Lee, "A Study on Characteristics of Eco-friendly Behaviors using Big Data: Focusing on the Customer Sales Data of Green CardStudy of GUI design convergence", Journal of Digital Convergence, Vol. 14, No. 1, pp. 151-161, 2016. https://doi.org/10.14400/JDC.2016.14.1.151
  2. D. I. Tak, "A Study on The Inluence of Convergence Benefit of Facebook Fan Page in Brand Attachment and Brand Commitment," Journal of the Korea Convergence Society , Vol. 6, No. 9, pp. 199-206, 2015. https://doi.org/10.15207/JKCS.2015.6.5.199
  3. S. W. Lee, S. H. Kim, "Finding Industries for Big Data Usage on the Basis of AHP", Journal of digital Convergence, Vol. 14, No. 7, pp. 21-27, 2016. https://doi.org/10.14400/JDC.2016.14.7.21
  4. M. H. Lee, "Vitalizations of Government Information Sharing, System Connection, and System Integration", The Korea Institute of Public Administration, 2014.
  5. S. K. Park, "Proposal of a mobility management scheme for sensor nodes in IoT(Internet of Things)", Journal of Convergence Society for SMB, Vol. 6, No. 4, pp. 59-64, Dec. 2017
  6. J. M. Kleinberg, "Authoriatative Sources in a Hyperlinked Environment", Journal of ACM, Vol, 46, Issue, 5, pp. 604-632, 1999 https://doi.org/10.1145/324133.324140
  7. http://em.wikipedia.org/wiki/HITS_algorithm
  8. J. Franke, G. Nakhaeizadeh, I. Renz, "Text Mining", Theoretical Aspects and Applications. Heidelberg: Physica-Verlag. 1-19. 2003
  9. L. S. Kim, "Convergence of Information Technology and Corporate Strategy", Journal of the Korea Convergence Society, Vol. 6, No. 6, pp. 17-26, 2015. https://doi.org/10.15207/JKCS.2015.6.6.017
  10. J. Han, M. Kamber, J. Pei, Data Mining: Concepts and Techniques 3rd Edition, 2011.
  11. Y. J. Kim, "Convergence of Business Information System Process using Knowledge- based Method", Journal of the Korea Convergence Society, Vol. 6, No. 4, pp. 65-71, 2015. https://doi.org/10.15207/JKCS.2015.6.4.065
  12. M. H. Lee, "A Study on N-Screen Convergence Application with Mobile WebApp Environment", Journal of the Korea Convergence Society, Vol. 6, No. 2, pp. 43-48, 2015. https://doi.org/10.15207/JKCS.2015.6.2.043
  13. T. Wu, Y. Chen, J. Han,"Association Mining in Large Databases: A Re-examination of Its Measures", In Proceedings of the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, pp. 621-628 (2007)
  14. https://dev.twitter.com/resources/twitter-libraries
  15. H. Nakagawa, "Automatic term recognition based on statistics of compound nouns", Terminology, Vol.6,No.2, pp.195-210 (2000) https://doi.org/10.1075/term.6.2.05nak
  16. https://code.google.com/p/mecab/