Text categorization based on topic model

In the text literature, many topic models were proposed to represent documents and words as topics or latent topics in order to process text effectively and accurately. In this paper, we propose LDACLM or Latent Dirichlet Allocation Category LanguageModel for text categorization and estimate parameters of models by variational inference. As a variant of Latent Dirichlet Allocation Model, LDACLM regards documents of category as Language Model and uses variational parameters to estimate maximum a posteriori of terms. In general, experiments show LDACLM model is effective and outperform Naïve Bayes with Laplace smoothing and Rocchio algorithm but little inferior to SVM for text categorization.