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Comparative Study on Arabic Text Classification: Challenges and Opportunities

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Classification Applications with Deep Learning and Machine Learning Technologies

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1071))

Abstract

There have been great improvements in web technology over the past years which heavily loaded the Internet with various digital contents of different fields. This made finding certain text classification algorithms that fit a specific language or a set of languages a difficult task for researchers. Text Classification or categorization is the practice of allocating a given text document to one or more predefined labels or categories, it aims to obtain valuable information from unstructured text documents. This paper presents a comparative study based on a list of chosen published papers that focus on improving Arabic text classifications, to highlight the given models and the used classifiers besides discussing the faced challenges in these types of researches, then this paper proposes the expected research opportunities in the field of text classification research. Based on the reviewed researches, SVM and Naive Bayes were the most widely used classifiers for Arabic text classification, while more effort is needed to develop and to implement flexible Arabic text classification methods and classifiers.

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Author information

Authors and Affiliations

  1. School of Computer Sciences, Universiti Sains Malaysia, 11800, George Town, Pulau Pinang, Malaysia

    Mohammed K. Bani Melhem & Laith Abualigah

  2. Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan

    Laith Abualigah

  3. Faculty of Information Technology, Middle East University, Amman, 11831, Jordan

    Laith Abualigah

  4. Sorbonne Center of Artificial Intelligence, Sorbonne University-Abu Dhabi, Abu Dhabi, United Arab Emirates

    Raed Abu Zitar

  5. Department of Computer and Information Science, Linköping University, Linköping, Sweden

    Abdelazim G. Hussien

  6. Faculty of Science, Fayoum University, Faiyum, Egypt

    Abdelazim G. Hussien

  7. IN3—Computer Science Department, Universitat Oberta de Catalunya, Castelldefels, Spain

    Diego Oliva

  8. Depto. de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Guadalajara, Jalisco, Mexico

    Diego Oliva

Authors
  1. Mohammed K. Bani Melhem
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  2. Laith Abualigah
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  3. Raed Abu Zitar
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  4. Abdelazim G. Hussien
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  5. Diego Oliva
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Corresponding author

Correspondence to Laith Abualigah .

Editor information

Editors and Affiliations

  1. Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan

    Laith Abualigah

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Melhem, M.K.B., Abualigah, L., Zitar, R.A., Hussien, A.G., Oliva, D. (2023). Comparative Study on Arabic Text Classification: Challenges and Opportunities. In: Abualigah, L. (eds) Classification Applications with Deep Learning and Machine Learning Technologies. Studies in Computational Intelligence, vol 1071. Springer, Cham. https://doi.org/10.1007/978-3-031-17576-3_10

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