Arabic text classification methods: Systematic literature review of primary studies

Alabbas, Waleed; Al-Khateeb, Haider; Mansour, Ali

doi:10.1109/cist.2016.7805072

Cited by 26 publications

(14 citation statements)

References 43 publications

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“…The results show that SVM outperformed the other classifiers for the genre identification task in classical Arabic texts with a general accuracy of 80% and a highest accuracy of 86%. This result supports the results in the Arabic text classification literature, which indicates that SVM outperforms other supervised classification algorithms [Alabbas, Al-Khateeb, Mansour et al (2016)]. The study also aimed to identify, for the best performing classifier, the SVM classifier, the optimal value of MFW for the task of genre identification for classical Arabic text.…”

Section: Resultssupporting

confidence: 83%

“…An accuracy of 86% is considered acceptable for the genre detection task, as the results reported in the literature are 69% for SVM on German novels using stylometric features of text [Hettinger, Becker, Reger et al (2015)], and 64% for SVM on genre identification for English using the the Brown Corpus [Wu, Markert and Sharoff (2010)]. For Arabic genre identification, there are no previous studies to compare to, however, comparing to Arabic text classification literature our results are also reasonable, as the accuracy of Arabic text classification ranges between 61% and 98% [Alabbas, Al-Khateeb, Mansour et al (2016)]. Comparing our results to attribution studies, our results are comparable to those reported in the literature, Howedi et al [Howedi and Mohd (2014)] report 76.67% accuracy for SVM, and [Ouamour and Sayoud (2012); Ouamour and Sayoud (2013)] indicate performance of 80% by an SVM variant.…”

Section: Discussionsupporting

confidence: 55%

“…Thus, our review of relevant work will include work in the fields of Arabic text classification and Arabic text attribution. A recent comprehensive review on Arabic text classification primary studies [Alabbas, Al-Khateeb, Mansour et al (2016)] indicates that the majority of Arabic text classification studies apply the classification task on datasets which use the Modern Standard Arabic variation rather than the Classical Arabic variation, therefore there is shortage on studies for Classical Arabic. Modern Standard Arabic (MSA) is the standard for Arabic-speaking countries and is the formal language used in news and most media.…”

Section: Related Workmentioning

confidence: 99%

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A Comparative Study of Machine Learning Methods for Genre Identification of Classical Arabic Text

Al-Yahya¹

2019

Computers, Materials &Amp; Continua

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The purpose of this study is to evaluate the performance of five supervised machine learning methods for the task of automated genre identification of classical Arabic texts using text most frequent words as features. We design an experiment for comparing five machine-learning methods for the genre identification task for classical Arabic text. We set the data and the stylometric features and vary the classification method to evaluate the performance of each method. Of the five machine learning methods tested, we can conclude that Support Vector Machine (SVM) are generally the most effective. The contribution of this work lies in the evaluation of the five machine learning methods for the task of genre identification for classical Arabic text using stylometric features.

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Section: Resultssupporting

confidence: 83%

Section: Discussionsupporting

confidence: 55%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Comparative Study of Machine Learning Methods for Genre Identification of Classical Arabic Text

Al-Yahya¹

2019

Computers, Materials &Amp; Continua

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“…For Arabic texts, text preprocessing usually involves the following: removing punctuation marks, diacritics and non-Arabic letters, excluding the words with length less than three, and eliminating stop-words [15]. Arabic TREC-2002 Light Stemmer [16] have been employed to return the words to their stems by removing the most frequent suffixes and prefixes.…”

Section: Preprocessingmentioning

confidence: 99%

Cross-Lingual Sentiment Classification from English to Arabic using Machine Translation

Al-Shabi¹,

Adel²,

Omar³

et al. 2017

ijacsa

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Abstract-Cross-lingual sentiment learning is becoming increasingly important due to the multilingual nature of usergenerated content on social media and the scarce resources for languages other than English. However, cross-lingual sentiment learning is a challenging task due to the different distribution between translated data and original data and due to the language gap, i.e. each language has its own ways to express sentiments. This work explores the adaptation of English resources for sentiment analysis to a new language, Arabic. The aim is to design a light model for cross-lingual sentiment classification from English to Arabic, without any manual annotation effort which, at the same time, is easy to build and does not require deep linguistic analysis. The ultimate goal is to find an optimal baseline model and to determine the relation between the noise in the translated data and the accuracy of sentiment classification. Different configurations of several factors are investigated including feature representation, feature reduction methods, and the learning algorithms to find the optimal baseline model. Experiments show that a good classification model can be obtained from translated data regardless of the artificial noise added by machine translation. The results also show a significant cost to automation, and thus the best path to future enhancement is through the inclusion of language-specific knowledge and resources.

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“…History of Malware evolution shows that many malicious software were written for fun or testing software behaviour. However, state-ofthe-art malware is also developed for financial gain (Alam et al, 2014c), political influence, enabling anti-social behaviour such as cyberstalking (al-Khateeb et al, 2016), or to sabotaging the defence systems of a country. Consequently, malware coding became extremely sophisticated.…”

Section: Introductionmentioning

confidence: 99%

Effective methods to detect metamorphic malware: a systematic review

Irshad¹,

Al-Khateeb²,

Mansour³

et al. 2018

IJESDF

Self Cite

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The succeeding code for metamorphic Malware is routinely rewritten to remain stealthy and undetected within infected environments. This characteristic is maintained by means of encryption and decryption methods, obfuscation through garbage code insertion, code transformation and registry modification which makes detection very challenging. The main objective of this study is to contribute an evidence-based narrative demonstrating the effectiveness of recent proposals. Sixteen primary studies were included in this analysis based on a pre-defined protocol. The majority of the reviewed detection methods used Opcode, Control Flow Graph (CFG) and API Call Graph. Key challenges facing the detection of metamorphic malware include code obfuscation, lack of dynamic capabilities to analyse code and application difficulty. Methods were further analysed on the basis of their approach, limitation, empirical evidence and key parameters such as dataset, Detection Rate (DR) and False Positive Rate (FPR).

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Arabic text classification methods: Systematic literature review of primary studies

Cited by 26 publications

References 43 publications

A Comparative Study of Machine Learning Methods for Genre Identification of Classical Arabic Text

A Comparative Study of Machine Learning Methods for Genre Identification of Classical Arabic Text

Cross-Lingual Sentiment Classification from English to Arabic using Machine Translation

Effective methods to detect metamorphic malware: a systematic review

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