PostRank
Mohsen Sayyadiharikandeh, Mohammad Ghodsi, Mohammad Naghibi · 2012
Dimension reduction techniques for text documents can be used for in the preprocessing phrase of blog mining, but these techniques can be more effective if they deal with the nature of the blogs properly. In this paper we propose a novel algorithm called PostRank using shallow approach to identify theme of the blog or blog representative words in order to reduce the dimensions of blogs. PostRank uses a graph-based syntactic representation of the weblog by taking into account some structural features of weblog. At the first step it models the blog as a complete graph and assumes the theme of the blog as a query applied to a search engine like Google and each post as a search result. It tries to rank the posts using Markov chain model like PageRank in Google. We used the ranking model under the assumption that top ranked nodes contain blog best representative words. Then it tries to identify post groups according to their scores. Finally this algorithm analyzes the first group using statistical methods(like TF-IDF) to identify blog representative words. Other groups are candidates of having blog theme after occurring change of theme to the blog. By arriving new instances of posts we try to update the blog graph by setting the initial scores of old nodes in the Markov chain to their final score from last run and continue the PostRank iterations until reaching convergence point. If half of the representative words have changed we would say that theme of the weblog has been changed.