Richness evaluation of blogs on its topics using a generative model and probabilistic analysis

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

Abstract

Nowadays, blogs are one of important web services to publish and share various information. Accordingly, evaluation of various keywords in blogs is one of the important research topics for effective and efficient classification and retrieval of blogs in the blogosphere. In this paper, we propose a method to identify important keywords in a blog. In order to identify such keywords, we consider web context, assuming that the blogs documents are generated from web contexts by proposed generative model. Therefore, if the contexts of keyword on the web are reflected well in the blog, then we may regard the keyword is essential because the blog is rich on the keyword. We clustered the blog articles on the given keyword by several subtopics using LDA (Latent Dirichlet Analysis), and compared the clusters with the web context documents obtained by web search. Finally, we evaluated the richness of blog on each keyword.

Original languageEnglish
Title of host publication6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012
Pages381-385
Number of pages5
DOIs
StatePublished - 2012
Event2012 Joint 6th International Conference on Soft Computing and Intelligent Systems, SCIS 2012 and 13th International Symposium on Advanced Intelligence Systems, ISIS 2012 - Kobe, Japan
Duration: 20 Nov 201224 Nov 2012

Publication series

Name6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012

Conference

Conference2012 Joint 6th International Conference on Soft Computing and Intelligent Systems, SCIS 2012 and 13th International Symposium on Advanced Intelligence Systems, ISIS 2012
Country/TerritoryJapan
CityKobe
Period20/11/1224/11/12

Keywords

  • Data Mining
  • Information Retrieval
  • Semantic Web
  • Text Mining

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