Topic optimization method based on Laplace score

Text classification algorithms based on topic models represent documents as topic vectors and use topic vectors to train classification models. One problem of topic based representation is (bat the topics generated by topic models have different qualities, so the topics with poor qualities will seriously affect the classification accuracy. To solve this problem, in this paper Laplace weight algorithm is proposed to calculate the weight of topics. We use the Laplace weight as the weights of topics, which can evaluate the importance of topics. The experiments show that the Laplace weight can improve the classification accuracy.