A Supervised Parameter Estimation Method of LDA

Zhenyan Liu, Dan Meng, Weiping Wang, Chunxia Zhang

Abstract: Latent Dirichlet Allocation (LDA) probabilistic topic model is a very effective dimension-reduction tool which can automatically extract latent topics and dedicate to text representation in a lower-dimensional semantic topic space. But the original LDA and its most variants are unsupervised without reference to category label of the documents in the training corpus. And most of them view the terms in vocabulary as equally important, but the weight of each term is different, especially for a skewed corpus in which there are many more samples of some categories than others. As a result, we propose a supervised parameter estimation method based on category and document information which can estimate the parameters of LDA according to term weight. The comparative experiments show that the proposed method is superior for the skewed text classification, which can largely improve the recall and precision of the minority category.