디지털융복합연구 (Journal of Digital Convergence)

  • 제13권12호
  • /
  • Pages.161-169
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  • 2015
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  • 2713-6434(pISSN)
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  • 2713-6442(eISSN)
한국디지털정책학회 (The Society of Digital Policy and Management)
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실시간 검색어 연관 분석을 통한 핵심 이슈 선정

Selecting a key issue through association analysis of realtime search words

  • 정민영 (광주여자대학교 실버케어학과)
  • 투고 : 2015.10.12
  • 심사 : 2015.12.20
  • 발행 : 2015.12.28
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⟨ 이전 논문 다음 논문 ⟩

초록

포털 사이트의 실시간 검색어는 현재 관심이 급상승하고 있는 이슈를 보여주기 위해 주로 검색횟수가 많은 순서에 따라 몇 초 간격으로 제공되고 있다. 그렇지만 너무 짧은 시간 내에 순위가 바뀌는 실시간 검색어의 특성 때문에 하루의 핵심 이슈를 비켜가는 문제가 발생한다. 본 논문에서 이러한 문제를 보완하기 위해 검색어들 사이의 연관 분석을 통하여 검색어들이 관련된 핵심 이슈를 도출하는 방법을 제안하고자 한다. 이를 위해 먼저 실시간 검색어를 순위와 상대적 관심도를 기반으로 점수화하여 집단별 기술통계를 통해 최상위 10개의 검색어를 도출한다. 그 다음으로 지지도와 신뢰도를 기반으로 연관 규칙을 추출하고 이를 가시화하는 그래프 결과를 바탕으로 핵심 이슈를 선정한다. 실험 결과는 단일 최상위 실시간 검색어보다 연관분석을 통해 높은 점수로 선정된 핵심 이슈가 더 큰 의미를 갖는다는 것을 보여준다.

Realtime search words of typical portal sites appear every few seconds in descending order by search frequency in order to show issues increasing rapidly in interest. However, the characteristics of realtime search words reordering within too short a time cause problems that they go over the key issues of the day. This paper proposes a method for deriving a key issue through association analysis of realtime search words. The proposed method first makes scores of realtime search words depending on the ranking and the relative interest, and derives the top 10 search words through descriptive statistics for groups. Then, it extracts association rules depending on 'support' and 'confidence', and chooses the key issue based on the results as a graph visualizing them. The results of experiments show that the key issue through association rules is more meaningful than the first realtime search word.

키워드

참고문헌

  1. Guandong Xu, Lin Li, and Yanchun Zhang, Web Mining and Social Networking: Techniques and Applications. Springer, 2011
  2. Han, Jiawei, and Chi Wang. "Mining latent entity structures from massive unstructured and interconnected data." Proceedings of the 2014 ACM SIGMOD international conference on Management of data. ACM, 2014
  3. Scott Spangler and Jeffrey Kreulen, "Mining the Talk: Unlocking the Business Value in Unstructured Information", IBM, 2007
  4. Ronen Feldman and James Sanger, "The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data", Cambridge University, 2007
  5. Kyoo-Sung Noh, "A Exploratory Study on Big-data based Election Campaign Strategy Model in South Korea ", Journal of Digital Convergence, v.11, no.12, 113-120, 2013 https://doi.org/10.14400/JDPM.2013.11.12.113
  6. Miner G, Elder J, Hill T, Nisbet R, Delen D, and Fast A, "Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications", Elsevier Academic Press, 2012
  7. Bing Liu, "Web Data Mining: Exploring Hyperlinks: Contents and Usage Data", Springer, 2011
  8. Guandong Xu, Lin Li, and Yanchun Zhang, "Web Mining and Social Networking: Techniques and Applications". Springer, 2011
  9. Su-Hyeon Namn, "Knowledge Creation Structure of Big Data Research Domain", Journal of Digital Convergence, v.13, no.9, 129-136, 2015 https://doi.org/10.14400/JDC.2015.13.9.129
  10. Bing Liu, "Sentiment Analysis and Subjectivity", Handbook of Natural Language Processing, 2010
  11. Reis Pinheiro and Carlos Andre, "Social Network Analysis in Telecommunications". John Wiley & Sons, 2011
  12. Golbandi, Nadav Golbandi, et al. "Expediting search trend detection via prediction of query counts." Proceedings of the sixth ACM international conference on Web search and data mining. ACM, 2013
  13. Naver Search Help, "Realtime hot searches", https://help.naver.com/support/service/main.nhn?serviceNo=606&categoryNo=1989, 2015
  14. Google Trends Help, Trends Searches, "https://support.google.com/trends/?hl=en#topic=6248107", 2015
  15. KISO Validation Committee, "The third validation report about realtime hot searches of Naver", 2014
  16. Lee, Changyong, Bomi Song, and Yongtae Park. "Design of convergent product concepts based on functionality: An association rule mining and decision tree approach." Expert Systems with Applications Vol. 39, No. 10, pp.9534-9542, 2012 https://doi.org/10.1016/j.eswa.2012.02.099
  17. Hahsler, Michael, and Sudheer Chelluboina. "Visualizing Association Rules: Introduction to the R-extension Package arulesViz.", R project module, pp.223-238, 2011
  18. KeunWon Kim, DongWoo Kim, Kyoo-Sung Noh, and Joo-Yeoun Lee, "An Exploratory Study on Improvement Method of the Subway Congestion Based Big Data Convergence", Journal of Digital Convergence, v.13, no.2, 35-42, 2015 https://doi.org/10.14400/JDC.2015.13.2.35