Title: Latent semantic text classification method research based on support vector machine

Authors: Qingmei Lu; Yulin Wang

Addresses: International School of Software, Wuhan University, Wu Han, 430072, China; School of Science and Control Engineering, North University of China, Taiyuan, 030051, China; Department of Bioengineering, University of Louisville, Louisville, 40292, USA ' International School of Software, Wuhan University, Wu Han, 430072, China

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.

Keywords: latent semantics analysis; LSA; semantics; vector machine; machine learning.

DOI: 10.1504/IJICT.2019.102998

International Journal of Information and Communication Technology, 2019 Vol.15 No.3, pp.243 - 255

Available online: 11 Oct 2019 *

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