A comparative study for Arabic text classification algorithms based on stop words elimination

Bassam Al-Shargabi; Waseem Al-Romimah; Fekry Olayah

doi:10.1145/1980822.1980833

A comparative study for Arabic text classification algorithms based on stop words elimination

Bassam Al-Shargabi^*, Waseem Al-Romimah, Fekry Olayah

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

33 Citations (Scopus)

Abstract

This paper compares three techniques for Arabic text classification; these techniques are Support Vector Machine (SVM) with Sequential Minimal Optimization (SMO), Naïve Bayesian (NB), and J48. The main objective of this paper is to measure the accuracy for each classifier and to determine which classifier is more accurate for Arabic text classification based on stop words elimination. The accuracy for classifier is measured by Percentage split method (holdout), and K-fold cross validation methods,. 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 the smallest time.

Original language	English
Title of host publication	Proceedings of the 2nd International Conference on Intelligent Semantic Web-Services and Applications, ISWSA'11
DOIs	https://doi.org/10.1145/1980822.1980833
Publication status	Published - 18 Apr 2011
Externally published	Yes
Event	2nd International Conference on Intelligent Semantic Web-Services and Applications, ISWSA 2011 - Amman, Jordan Duration: 18 Apr 2011 → 20 Apr 2011

Conference

Conference	2nd International Conference on Intelligent Semantic Web-Services and Applications, ISWSA 2011
Country/Territory	Jordan
City	Amman
Period	18/04/11 → 20/04/11

Keywords

Arabic text classification
Naive Bayesian
Stop word elimination
Support vector machine

Access to Document

10.1145/1980822.1980833

Cite this

@inproceedings{9677f5e67dcb4b6bba477979fad5fcd6,

title = "A comparative study for Arabic text classification algorithms based on stop words elimination",

abstract = "This paper compares three techniques for Arabic text classification; these techniques are Support Vector Machine (SVM) with Sequential Minimal Optimization (SMO), Na{\"i}ve Bayesian (NB), and J48. The main objective of this paper is to measure the accuracy for each classifier and to determine which classifier is more accurate for Arabic text classification based on stop words elimination. The accuracy for classifier is measured by Percentage split method (holdout), and K-fold cross validation methods,. 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 the smallest time.",

keywords = "Arabic text classification, Naive Bayesian, Stop word elimination, Support vector machine",

author = "Bassam Al-Shargabi and Waseem Al-Romimah and Fekry Olayah",

year = "2011",

month = apr,

day = "18",

doi = "10.1145/1980822.1980833",

language = "English",

isbn = "9781450304740",

booktitle = "Proceedings of the 2nd International Conference on Intelligent Semantic Web-Services and Applications, ISWSA'11",

note = "2nd International Conference on Intelligent Semantic Web-Services and Applications, ISWSA 2011 ; Conference date: 18-04-2011 Through 20-04-2011",

}

Al-Shargabi, B, Al-Romimah, W & Olayah, F 2011, A comparative study for Arabic text classification algorithms based on stop words elimination. in Proceedings of the 2nd International Conference on Intelligent Semantic Web-Services and Applications, ISWSA'11., 11, 2nd International Conference on Intelligent Semantic Web-Services and Applications, ISWSA 2011, Amman, Jordan, 18/04/11. https://doi.org/10.1145/1980822.1980833

A comparative study for Arabic text classification algorithms based on stop words elimination. / Al-Shargabi, Bassam; Al-Romimah, Waseem; Olayah, Fekry.
Proceedings of the 2nd International Conference on Intelligent Semantic Web-Services and Applications, ISWSA'11. 2011. 11.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A comparative study for Arabic text classification algorithms based on stop words elimination

AU - Al-Shargabi, Bassam

AU - Al-Romimah, Waseem

AU - Olayah, Fekry

PY - 2011/4/18

Y1 - 2011/4/18

N2 - This paper compares three techniques for Arabic text classification; these techniques are Support Vector Machine (SVM) with Sequential Minimal Optimization (SMO), Naïve Bayesian (NB), and J48. The main objective of this paper is to measure the accuracy for each classifier and to determine which classifier is more accurate for Arabic text classification based on stop words elimination. The accuracy for classifier is measured by Percentage split method (holdout), and K-fold cross validation methods,. 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 the smallest time.

AB - This paper compares three techniques for Arabic text classification; these techniques are Support Vector Machine (SVM) with Sequential Minimal Optimization (SMO), Naïve Bayesian (NB), and J48. The main objective of this paper is to measure the accuracy for each classifier and to determine which classifier is more accurate for Arabic text classification based on stop words elimination. The accuracy for classifier is measured by Percentage split method (holdout), and K-fold cross validation methods,. 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 the smallest time.

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KW - Naive Bayesian

KW - Stop word elimination

KW - Support vector machine

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DO - 10.1145/1980822.1980833

M3 - Conference contribution

AN - SCOPUS:79958019969

SN - 9781450304740

BT - Proceedings of the 2nd International Conference on Intelligent Semantic Web-Services and Applications, ISWSA'11

T2 - 2nd International Conference on Intelligent Semantic Web-Services and Applications, ISWSA 2011

Y2 - 18 April 2011 through 20 April 2011

ER -

A comparative study for Arabic text classification algorithms based on stop words elimination

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Keywords

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