Automatic Arabic document categorization based on the Naïve Bayes algorithm

Kourdi, M. El; Bensaid, A.; Rachidi, Tajjeeddine

doi:10.3115/1621804.1621819

Cited by 145 publications

(106 citation statements)

References 14 publications

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“…The Naïve Bayesian (NB) applied in this study to classify 300 of the Arabic web document that taken from Al-Jazeera website "the channel of Arabic News in Qatar Television" into five categories "Science, Health, Culture and Art, Business, and Sport". The results showed accuracy in classification reaches 92.8% while the manual methods reached 62.8 [7].…”

Section: Related Workmentioning

confidence: 80%

A Method of Automatic Domain Extraction of Text to Facilitate Retrieval of Arabic Documents

Al-Maghasbeh¹,

Hamzah²

2018

ijacsa

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Abstract-Arabic content on the internet has increased over the web because of the growth of the number of Arabic persons who use the internet in the world. Accordingly, this study introduces an automatic approach of domain extraction of information retrieval from these contents based on text classification. Text classification process makes the searching domain specific to facilitate the searching process. This paper discusses how to enhance the capacity of information retrieval in Arabic documents by classifying the unlabelled Arabic text automatically by using text classification algorithms. The classification of documents and texts is an important field in computer science and information retrieval. It aims at enhancing the retrieval process by identifying the searching-domain of retrieval systems.

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Section: Related Workmentioning

confidence: 80%

A Method of Automatic Domain Extraction of Text to Facilitate Retrieval of Arabic Documents

Al-Maghasbeh¹,

Hamzah²

2018

ijacsa

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“…Elkourdi, Bensaid, andRachidi [6] implemented Naïve Bayes algorithms in classifying Arabic documents and reported 68.8% accuracy.…”

Section: Related Workmentioning

confidence: 99%

Arabic Document Classification Using Multiword Features

Abuaiadah¹

2013

IJCCE

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Abstract-Weinvestigate the use of multiword features to improve Arabic document classification. The Arabic language is both morphologically rich and highly inflected. Accordingly it presents more challenges when enhancing Arabic information retrieval to a level comparable to English. The multiword features are modeled as a combination of words appearing within windows of varying sizes. Our experiments show multiword features combined with dice similarity distance outperform the cosine similarity function and produce results that are comparable to TF-IDF representation. Multiword features are under-explored and we believe they have the potential to improve Arabic information retrieval and, in particular, Arabic document classification.

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“…Then, The Arabic stop words are removed. Some Arabic documents may contain foreign words, special characters, numbers [23,24]. Finally, words with length less than three letters are eliminated, often these words are not important and are not useful in TC.…”

Section: A Text Preprocessingmentioning

confidence: 99%

Arabic Text Categorization Using Mixed Words

Hussein¹,

Mousa²,

Sallam³

2016

IJITCS

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Abstract-There is a tremendous number of Arabic text documents available online that is growing every day. Thus, categorizing these documents becomes very important. In this paper, an approach is proposed to enhance the accuracy of the Arabic text categorization. It is based on a new features representation technique that uses a mixture of a bag of words (BOW) and two adjacent words with different proportions. It also introduces a new features selection technique depends on Term Frequency (TF) and uses Frequency Ratio Accumulation Method (FRAM) as a classifier. Experiments are performed without both of normalization and stemming, with one of them, and with both of them. In addition, three data sets of different categories have been collected from online Arabic documents for evaluating the proposed approach. The highest accuracy obtained is 98.61% by the use of normalization.

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Automatic Arabic document categorization based on the Naïve Bayes algorithm

Cited by 145 publications

References 14 publications

A Method of Automatic Domain Extraction of Text to Facilitate Retrieval of Arabic Documents

A Method of Automatic Domain Extraction of Text to Facilitate Retrieval of Arabic Documents

Arabic Document Classification Using Multiword Features

Arabic Text Categorization Using Mixed Words

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