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Related Term Extraction with Proximity Matrix for Query Related Issue Detection using Twitter

트위터를 이용한 질의어 관련 이슈 탐지를 위한 인접도 행렬 기반 연관 어휘 추출

  • 김제상 (금오공과대학교 컴퓨터공학부) ;
  • 조효근 (금오공과대학교 컴퓨터공학부) ;
  • 김동성 (금오공과대학교 컴퓨터공학부) ;
  • 김병만 (금오공과대학교 컴퓨터소프트웨어공학과) ;
  • 이현아 (금오공과대학교 컴퓨터소프트웨어공학과)
  • Received : 2013.08.12
  • Accepted : 2013.11.14
  • Published : 2014.01.31

Abstract

Social network services(SNS) including Twitter and Facebook are good resources to extract various issues like public interest, trend and topic. This paper proposes a method to extract query-related issues by calculating relatedness between terms in Twitter. As a term that frequently appears near query terms should be semantically related to a query, we calculate term relatedness in retrieved documents by summing proximity that is proportional to term frequency and inversely proportional to distance between words. Then terms, relatedness of which is bigger than threshold, are extracted as query-related issues, and our system shows those issues with a connected network. By analyzing single transitions in a connected network, compound words are easily obtained.

트위터와 페이스북 등의 SNS(Social Network Service)는 일반 대중의 관심사나 트렌드 등의 이슈를 탐지하기 좋은 지식원이다. 본 논문에서는 검색 질의어에 관련된 이슈나 화제를 질의어에 대한 연관 어휘로 보고, 이를 트위터에서 추출하기 위한 방법을 제안한다. 제안하는 방법에서는 질의어와 연관성이 높은 단어는 질의어와 가까운 위치에서 자주 발생한다고 가정하고, 단어 간 거리에 반비례하고 공기 빈도에 비례하는 단어 간 인접도의 합으로 단어간 연관도를 구한다. 구해진 연관도 값이 임계치를 넘는 어휘를 연관 어휘로 보고 네트워크의 형태로 관련 이슈를 제시한다. 제안한 방법에서는 네트워크의 특성을 분석하여 복합어를 손쉽게 탐지할 수 있다.

Keywords

References

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