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


KIPS Transactions on Software and Data Engineering, Vol. 3, No. 1, pp. 31-36, Jan. 2014
10.3745/KTSDE.2014.3.1.31, Full Text:

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.


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Cite this article
[IEEE Style]
J. S. Kim, H. G. Jo, D. S. Kim, B. M. Kim and H. A. Lee, "Related Term Extraction with Proximity Matrix for Query Related Issue Detection using Twitter," KIPS Transactions on Software and Data Engineering, vol. 3, no. 1, pp. 31-36, 2014. DOI: 10.3745/KTSDE.2014.3.1.31.

[ACM Style]
Je Sang Kim, Hyo Geun Jo, Dong Sung Kim, Byeong Man Kim, and Hyun Ah Lee. 2014. Related Term Extraction with Proximity Matrix for Query Related Issue Detection using Twitter. KIPS Transactions on Software and Data Engineering, 3, 1, (2014), 31-36. DOI: 10.3745/KTSDE.2014.3.1.31.