The documents classification algorithm based on LDA
HE Jin-qu · Journal of tianjin University of Technology · 2014
Latent Dirichlet Allocation is a classic topic model which can extract latent topic from large data corpus.Model assumes that if a document is relevant to a topic, then all tokens in the document are relevant to that topic.Through narrowing the generate scope that each document generated from, in this paper, we present an improved text classification algorithm for adding topic-category distribution parameter to Latent Dirichlet Allocation. Documents in this model are generated from the category they most relevant. Gibbs sampling is employed to conduct approximate inference. And preliminary experiment is presented at the end of this paper.