The Application of TF-IDF with Time Factor in the Cluster of Micro-blog Theme

Abstract

Time factor is of great significance for the topic clustering for Micro-blog. Usually, the topics discussed most frequently during a certain period may become the hot issues. Therefore, this article has successfully obtained the method of TF-IDF-TF by different division of periods and setting of different weights, then applied it to the ULPIR Microblog content corpus, with the hierarchical clustering method and k-means method being used to make statistic analysis. The result of the experiment shows that, compared with the traditional TF-IDF( term frequency- inverse document frequency ), the TF-IDF-TF method could provide more accurate clustering result, especially for specific topics during the period when users play most frequently.