Two Text Classifiers in Online Discussion: Support Vector Machine vs Back-Propagation Neural Network
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.
Author: E. Erlin, R. Rahmiati,
Unang Rio
Journal Code: jptkomputergg140034
