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Comparative evaluation of text classification techniques using a large diverse Arabic dataset.

A vast amount of valuable human knowledge is recorded in documents. The rapid growth in the number of machine-readable documents for public or private access necessitates the use of automatic text classification. While a lot of effort has been put into Western languages-mostly English-minimal experi...

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Published in:Language Resources & Evaluation Vol. 47; no. 2; pp. 513 - 539
Main Authors: Khorsheed, Mohammad, Al-Thubaity, Abdulmohsen
Format: Article
Published: Springer Nature Jun2013
Subjects:
Machine learning
Arabic language
Language & languages
Algorithms
Decision trees
Comparative studies
Online Access:View this record in EBSCOhost
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      dt: Jun2013
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      pub: Springer Nature
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        87846092
        10.1007/s10579-013-9221-8
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        atl: Comparative evaluation of text classification techniques using a large diverse Arabic dataset.
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          Khorsheed, Mohammad
          Al-Thubaity, Abdulmohsen
        affil: King Abdulaziz City for Science & Technology, Riyadh 11442 Saudi Arabia
      su:
        Machine learning
        Arabic language
        Language & languages
        Algorithms
        Decision trees
        Comparative studies
      sug:
        subj:
          Machine learning
          Arabic language
          Language & languages
          Algorithms
          Decision trees
          Comparative studies
      keyword:
        Arabic text categorization
        Arabic text classification
      ab: A vast amount of valuable human knowledge is recorded in documents. The rapid growth in the number of machine-readable documents for public or private access necessitates the use of automatic text classification. While a lot of effort has been put into Western languages-mostly English-minimal experimentation has been done with Arabic. This paper presents, first, an up-to-date review of the work done in the field of Arabic text classification and, second, a large and diverse dataset that can be used for benchmarking Arabic text classification algorithms. The different techniques derived from the literature review are illustrated by their application to the proposed dataset. The results of various feature selections, weighting methods, and classification algorithms show, on average, the superiority of support vector machine, followed by the decision tree algorithm (C4.5) and Naïve Bayes. The best classification accuracy was 97 % for the Islamic Topics dataset, and the least accurate was 61 % for the Arabic Poems dataset.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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      custom: Language Resources & Evaluation is a copyright of Springer, 2013. All Rights Reserved.
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      holder: Springer Nature
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          year: 2013
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