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http://dx.doi.org/10.3745/KTSDE.2014.3.1.31

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

Kim, Je-Sang (금오공과대학교 컴퓨터공학부)
Jo, Hyo-Geun (금오공과대학교 컴퓨터공학부)
Kim, Dong-Sung (금오공과대학교 컴퓨터공학부)
Kim, Byeong Man (금오공과대학교 컴퓨터소프트웨어공학과)
Lee, Hyun Ah (금오공과대학교 컴퓨터소프트웨어공학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.3, no.1, 2014 , pp. 31-36 More about this Journal
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.
Keywords
Related Term; Proximity Matrix; Query Related Issue; Issue Detection; SNS;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
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