Topic word selection for blogs by topic richness using web search result clustering

Jinhee Park, Sungwoo Lee, Hye Wuk Jung, Jee Hyong Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Blogs are one of popular services to publish and archive posts with personal opinions on the web. The topics of a blog can be used for classification, recommendation, opinion mining, and ranking, etc. In this paper, we propose a method for extracting important topic words from a blog. Our method selects topic words by measuring whether the blog includes rich content on the word. To measure the richness of a blog on candidate topic words, we compare web search results by the candidate words with the content of the blog. We used document clustering and cluster matching in order to compare them.

Original languageEnglish
Title of host publicationProceedings of the 6th International Conference on Ubiquitous Information Management and Communication, ICUIMC'12
DOIs
StatePublished - 2012
Externally publishedYes
Event6th International Conference on Ubiquitous Information Management and Communication, ICUIMC'12 - Kuala Lumpur, Malaysia
Duration: 20 Feb 201222 Feb 2012

Publication series

NameProceedings of the 6th International Conference on Ubiquitous Information Management and Communication, ICUIMC'12

Conference

Conference6th International Conference on Ubiquitous Information Management and Communication, ICUIMC'12
Country/TerritoryMalaysia
CityKuala Lumpur
Period20/02/1222/02/12

Keywords

  • Data mining
  • Information retrieval topic extraction
  • Result clustering
  • Text mining

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