Article

GSTF Journal on Computing (JoC)

, 5:16

First online:

Open Access This content is freely available online to anyone, anywhere at any time.

Arabic Text Categorization Using Support vector machine, Naïve Bayes and Neural Network

  • Adel Hamdan MohammadAffiliated withComputer Science Department, The world Islamic Sciences and Education University
  • , Tariq Alwada‘nAffiliated withComputer Science Department, The world Islamic Sciences and Education University
  • , Omar Al-MomaniAffiliated withNetwork Department, The world Islamic Sciences and Education University

Abstract

Text classification is a very important area in information retrieval. Text classification techniques used to classify documents into a set of predefined categories. There are several techniques and methods used to classify data and in fact there are many researches talks about English text classification. Unfortunately, few researches talks about Arabic text classification. This paper talks about three well-known techniques used to classify data. These three well-known techniques are applied on Arabic data set. A comparative study is made between these three techniques. Also this study used fixed number of documents for all categories of documents in training and testing phase. The result shows that the Support Vector machine gives the best results.

Keywords:

Text Categorization Naïve Bayesian Text Classification Support Vector Machine Multilayer Perceptron Neural Network