A Hierarchy Method Based on LDA and SVM for News Classification

Author

Limeng Cui ; Fan Meng ; Yong Shi ; Minqiang Li ; An Liu

Author_Institution

Res. Center on Fictitious Econ. & Data Sci., Key Res. Lab. on Big Data Min. & Knowledge Manage., Beijing, China

fYear

2014

fDate

14-14 Dec. 2014

Firstpage

60

Lastpage

64

Abstract

He growth of the online data provides the user a access to information on the Internet but also creates the challenges to obtain the valuable knowledge. In this paper we focus on news text classification, which is meaningful for information provider to organize and display the news but also for the users to reach the valuable information easily. A hierarchy method based on LDA and SVM is proposed to accomplish this task and several experiments are conducted to evaluate our method. The results show that our method is promising in text classification problems.

Keywords

Internet; pattern classification; probability; support vector machines; text analysis; Internet; LDA; SVM; hierarchy method; latent Dirichlet allocation; news text classification; online data; support vector machine; Classification algorithms; Computational modeling; Kernel; Runtime; Support vector machines; Text categorization; LDA; News classification; SVM;

fLanguage

English

Publisher

ieee

Conference_Titel

Data Mining Workshop (ICDMW), 2014 IEEE International Conference on

Conference_Location

Shenzhen

Print_ISBN

978-1-4799-4275-6

Type

conf

DOI

10.1109/ICDMW.2014.8

Filename

7022579