Research on Text Classification Method of LDA- SVM Based on PSO optimization

摘要

The LDA (Latent Dirichlet Allocation) model is an unsupervised learning model that can extract potential topics in the corpus. It is widely used in natural language processing applications. The combination of SVM (Support Vector Machines) and LDA has better text classification effect. However, because the mature kernel function of SVM and its parameter selection can only be selected according to experience, with certain randomness, it has a great influence on classification accuracy. This paper proposes an optimized LDA-SVM text classification method using Particle Swarm optimization (PSO). The algorithm (PSO) optimizes the SVM error penalty parameter C to improve the LDA-SVM text classification algorithm. The experimental results show that the LDA-PSO-SVM text classification algorithm proposed in this paper has higher accuracy and better classification performance than other algorithms.