Latent semantic text classification method research based on support vector machine

Abstract

Text classification, as an important process of network public opinion analysis, will directly affect the judgment of text public opinion. The accuracy of text classification is an important prerequisite for textual public opinion analysis. At present, the commonly used text classification methods mainly focus on clustering and machine learning. In general, the accuracy is not ideal. Moreover, text classification method based on latent semantics has the characteristics of insensitivity to feature dimension and simple classification methods, so it has become the focus of extensive research. However, as the type of text increases, local semantic analysis will occur, resulting in the dropping of classification accuracy of text. In this paper, a latent semantic classification method based on support vector machine (LR-LSA) is proposed to solve the problem of local semantic analysis brought by too much text category, it can be better to solve the impact of feature dimension surge on effect.