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    Arabic text classification methods: systematic literature review of primary studies

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    Authors
    Alabbas, Waleed
    al-Khateeb, Haider
    Mansour, Ali
    Affiliation
    University of Bedfordshire
    Issue Date
    2017-01-05
    Subjects
    systematic literature review
    Arabic text classification
    data mining
    text corpus
    big data
    
    Metadata
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    Abstract
    Recent research on Big Data proposed and evaluated a number of advanced techniques to gain meaningful information from the complex and large volume of data available on the World Wide Web. To achieve accurate text analysis, a process is usually initiated with a Text Classification (TC) method. Reviewing the very recent literature in this area shows that most studies are focused on English (and other scripts) while attempts on classifying Arabic texts remain relatively very limited. Hence, we intend to contribute the first Systematic Literature Review (SLR) utilizing a search protocol strictly to summarize key characteristics of the different TC techniques and methods used to classify Arabic text, this work also aims to identify and share a scientific evidence of the gap in current literature to help suggesting areas for further research. Our SLR explicitly investigates empirical evidence as a decision factor to include studies, then conclude which classifier produced more accurate results. Further, our findings identify the lack of standardized corpuses for Arabic text; authors compile their own, and most of the work is focused on Modern Arabic with very little done on Colloquial Arabic despite its wide use in Social Media Networks such as Twitter. In total, 1464 papers were surveyed from which 48 primary studies were included and analyzed.
    Citation
    Alabbas W, Al-Khateeb HM, Mansour A (2016) 'Arabic text classification methods: Systematic literature review of primary studies', 4th IEEE International Colloquium on Information Science and Technology (CiSt) - Tangier, Institute of Electrical and Electronics Engineers Inc..
    Publisher
    Institute of Electrical and Electronics Engineers Inc.
    URI
    http://hdl.handle.net/10547/624325
    DOI
    10.1109/CIST.2016.7805072
    Additional Links
    https://ieeexplore.ieee.org/document/7805072
    Type
    Conference papers, meetings and proceedings
    Language
    en
    ISBN
    9781509007516
    ae974a485f413a2113503eed53cd6c53
    10.1109/CIST.2016.7805072
    Scopus Count
    Collections
    Computing

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