Web Text Classification based on LDA Model
Rui Zhou · Journal of Yancheng Institute of Technology · 2009
A kind of web text classification is put forward on the basis of LDA model.Latent Dirichlet Allocation(LDA) is an unsupervised topic learning model which extracts latent topics from text data.Parameters are estimated with Gibbs sampling of MCMC and the word probability is represented.Thus different latent topics are associated with observable words.Contrasting to SVM and Bayesian Network,the result in the experiment shows that LDA has the better performance than any other algorithm.