A Tag-based Improved LDA and Web Page Clustering Analysis

With the rapid development of Internet, tag technology has been widely used in various sites. The brief text labels of network resources are greatly convenient for people to access the massive data. Social tags allows the user to use any word ----to tag network objects, and to share these tags, because of its simple and flexible operation, and it has become one of the popular applications. However, there exists some problems like noise of tags, lack of using criteria, and sparse distribution etc. Especially sparsity of tags seriously limits its application in the semantic analysis of web pages. This paper, by exploiting the user-related tag expansion method to overcome this problem, at the same time by using the topic model----LDA to model the web tags, mine its potential topic from the large-scale web page, and obtain the topic distribution of the text to the text clustering analysis. The experimental results show that, compared with the traditional clustering algorithm, the method of based LDA clustering on the analysis of the web tags have a larger increase.