An experimental study for Arabic text classification techniques

Text classification is a process of classifying textual contents to a set of predefined classes and categories. As enormous numbers of documents and contextual contents are introduced every day on the Internet, it becomes essential to use text classification techniques for different purposes such as enhancing search retrieval and recommendation systems. A lot of work has been done to study different aspects of English text classification techniques. However, little attention has been devoted to study Arabic text classification due to the difficulty of processing Arabic language. Consequently, in this paper, we propose an enhanced Arabic topic-discovery architecture (EATA) that can use ontology to provide an effective Arabic topic classification mechanism. We have introduced a semantic enhancement model to improve Arabic text classification and the topic discovery technique by utilizing the rich semantic information in Arabic ontology. We rely in this study on the vector space model (term frequency-inverse document frequency (TF-IDF)) as well as the cosine similarity approach to classify new Arabic textual documents.

Download Full-text

An Experimental Study for the Effect of Stop Words Elimination for Arabic Text Classification Algorithms

Network and Communication Technology Innovations for Web and IT Advancement ◽

10.4018/978-1-4666-2157-2.ch012 ◽

2014 ◽

pp. 184-190

Author(s):

Bassam Al-Shargabi ◽

Fekry Olayah ◽

Waseem AL Romimah

Keyword(s):

Experimental Study ◽

Support Vector Machine ◽

Text Classification ◽

Error Rate ◽

Support Vector ◽

Arabic Text ◽

Sequential Minimal Optimization ◽

Split Method ◽

Arabic Text Classification ◽

Fold Cross Validation

In this paper, an experimental study was conducted on three techniques for Arabic text classification. These techniques are Support Vector Machine (SVM) with Sequential Minimal Optimization (SMO), Naïve Bayesian (NB), and J48. The paper assesses the accuracy for each classifier and determines which classifier is more accurate for Arabic text classification based on stop words elimination. The accuracy for each classifier is measured by Percentage split method (holdout), and K-fold cross validation methods, along with the time needed to classify Arabic text. The results show that the SMO classifier achieves the highest accuracy and the lowest error rate, and shows that the time needed to build the SMO model is much lower compared to other classification techniques.

Download Full-text

An Experimental Study for the Effect of Stop Words Elimination for Arabic Text Classification Algorithms

International Journal of Information Technology and Web Engineering ◽

10.4018/jitwe.2011040106 ◽

2011 ◽

Vol 6 (2) ◽

pp. 68-75 ◽

Cited By ~ 6

Author(s):

Bassam Al-Shargabi ◽

Fekry Olayah ◽

Waseem AL Romimah

Keyword(s):

Experimental Study ◽

Text Classification ◽

Cross Validation ◽

Support Vector ◽

Classification Algorithms ◽

Arabic Text ◽

Sequential Minimal Optimization ◽

Split Method ◽

Arabic Text Classification ◽

Fold Cross Validation

In this paper, an experimental study was conducted on three techniques for Arabic text classification. These techniques are Support Vector Machine (SVM) with Sequential Minimal Optimization (SMO), Naïve Bayesian (NB), and J48. The paper assesses the accuracy for each classifier and determines which classifier is more accurate for Arabic text classification based on stop words elimination. The accuracy for each classifier is measured by Percentage split method (holdout), and K-fold cross validation methods, along with the time needed to classify Arabic text. The results show that the SMO classifier achieves the highest accuracy and the lowest error rate, and shows that the time needed to build the SMO model is much lower compared to other classification techniques.

Download Full-text