TELKOMNIKA (Telecommunication Computing Electronics and Control)
Vol 12, No 1: March 2014

Two Text Classifiers in Online Discussion: Support Vector Machine vs Back-Propagation Neural Network

E. Erlin (STMIK-AMIK Riau)
R. Rahmiati (STMIK-AMIK Riau)
Unang Rio (STMIK-AMIK Riau)



Article Info

Publish Date
01 Mar 2014

Abstract

The purpose of this research is to compare the performance of two text classifiers; support vector machine (SVM) and back-propagation neural network (BPNN) within categorize messages from an online discussion. SVM has been recognized as one of the best algorithm for text categorization. BPNN is also a popular categorization method that can handle linear and non linear problems and can achieve good result. However, using SVM and BPNN in online discussion is rare. In this research, several SVM data are trained in multi-class categorization to classify the same set with BPNN. The effectiveness of these two text classifiers are measured and then statistically compared based on error rate, precision, recall and F-measure. The experimental result shows that for text message categorization in online discussion, the performances of SVM outperform BPNN in term of error rate and precision; and falls behind BPNN in term of recall and F-measure.

Copyrights © 2014






Journal Info

Abbrev

TELKOMNIKA

Publisher

Subject

Computer Science & IT

Description

Submitted papers are evaluated by anonymous referees by single blind peer review for contribution, originality, relevance, and presentation. The Editor shall inform you of the results of the review as soon as possible, hopefully in 10 weeks. Please notice that because of the great number of ...