Classifying the Arabic web &amp;#x2014; A pilot study

Abdeen, M.; Elsehemy, A.; Nazmy, Taymoor; Yagoub, M.C.E.

doi:10.1109/ccece.2011.6030580

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…In [53] the authors' main objective was to classify the Internet content of the Arabic text. They used Internet data of size 40 Gigabytes and classified them into five categories, namely, religion, politics, culture, sports, and economics.…”

Section: Previous Workmentioning

confidence: 99%

A Closer Look at Arabic Text Classification

Abdeen¹,

Albouq²,

Elmahalawy³

et al. 2019

IJACSA

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The world has witnessed an information explosion in the past two decades. Electronic devices are now available in many varieties such as PCs, Laptops, book readers, mobile devices and with relatively affordable prices. This and the ubiquitous use of software applications such as social media and cloud applications, and the increasing trend towards digitalization, the amount of information on the global cloud has surged to an unprecedented level. Therefore, a dire need exists in order to mine this massively large amount of data and produce meaningful information. Text Classification is one of the known and well established data mining techniques that has been used and reported in the literature. Text classification methods include statistical and machine learning algorithms such as Naive Baysian, Support Vector Machines and others have widely been used. Many works have been reported regarding text classification of various languages including English, Chinese, Russian, and many others. Arabic is the fifth most spoken language in the world. There has been many works in the literature for Arabic text classification. However, and to the best of our knowledge, there is no recent work that presents a good, critical and comprehensive survey of the Arabic text classification for the past two decades. The aim of this paper is to present a concise and yet comprehensive review of the Arabic text classification. We have covered over 50 research papers covering the past two decades (2000 -2019). The main focus of this paper is to address the following issues: 1) The techniques reported in the literature including. 2) New Techniques. 3) Most claimed efficient technique. 4) Datasets used and which ones are most popular. 5) Which feature selection techniques are used? 6) Popular classes/categories used. 7) Effect of stemming techniques on classification results.

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Section: Previous Workmentioning

confidence: 99%

A Closer Look at Arabic Text Classification

Abdeen¹,

Albouq²,

Elmahalawy³

et al. 2019

IJACSA

View full text Add to dashboard Cite

show abstract

“…Classification [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33] , [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [18], [48], [49], [50] 31(29%) Stemming and Lemmatization [51], [52], [53], [54], [55], [4], [56], [57], [58], [59], [60], [61] 12(11%) Information Retrieval [9], [62], [63], [64], [65], [66], [67], …”

Section: Techniquementioning

confidence: 99%

Arabic Text Mining a Systematic Review of the Published Literature 2002-2014

AlMahmoud

Al-Razgan

2015

2015 International Conference on Cloud Computing (ICCC)

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Text Mining is a set of techniques that analyzes large masses of data, extract relations that are unknown beforehand, and provide solutions to help decision-making. Text mining had been used extensively to analyze English text. However, text mining has only been used recently in analyzing Arabic text. As a result the objective of this paper is to present the current state of Arabic text mining. A systematic review has been performed to collect the papers published on the analysis of Arabic text mining. More than one hundred papers were used in our review from different reliable sources, and then they were classified according to their specific domain, and classified again according to the specific techniques used. This paper also provides quantitative analysis of publications according to publication type, year, category, and contributors.

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“…The Arabic web presents an important portion of the web. With Arabic as the 5th most spoken language in the world and with the increasing number of Arabic internet users and the increasing number of Arabic documents available in the internet at exponential rates, it has become more and more interesting for Arabic users to find relevant Arabic documents [11]. The Arabic Language morphology is so different than Latin Language [12].…”

Section: Suffix Tree Clustering For Arabic Languagementioning

confidence: 99%