@article {Boudad20182479,
	title = {Sentiment analysis in Arabic: A review of the literature},
	journal = {Ain Shams Engineering Journal},
	volume = {9},
	number = {4},
	year = {2018},
	pages = {2479-2490},
	doi = {10.1016/j.asej.2017.04.007},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85025446495\&doi=10.1016\%2fj.asej.2017.04.007\&partnerID=40\&md5=4c3f2d43d536d6eec5f42878492ea75c},
	author = {Boudad, N. and Faizi, R. and Oulad Haj Thami, R. and Chiheb, R.}
}
@article {Boudad2017,
	title = {Sentiment analysis in Arabic: A review of the literature},
	journal = {Ain Shams Engineering Journal},
	year = {2017},
	note = {cited By 0; Article in Press},
	abstract = {Within the last couple of years, Sentiment Analysis in Arabic has gained a considerable interest from the research community. In this respect, the objective of this paper is to provide a review of the major works that have dealt with this research area in this language. A thorough investigation of the available literature revealed that the works were mainly concentrated on dealing with specific Sentiment Analysis tasks. To this end, they used three different approaches, namely supervised, unsupervised and hybrid. The results that these studies achieved are interesting but divergent. This divergence is relatively due to the type of approach opted for, the task that is being analysed as well as to the specificities and intricacies of the Arabic variety understudy. {\textcopyright} 2017 Ain Shams University.},
	doi = {10.1016/j.asej.2017.04.007},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85025446495\&doi=10.1016\%2fj.asej.2017.04.007\&partnerID=40\&md5=4c3f2d43d536d6eec5f42878492ea75c},
	author = {Boudad, N. and Faizi, R. and Oulad Haj Thami, R. and Chiheb, R.}
}
@article {Boudad2017233,
	title = {Sentiment classification of Arabic tweets: A supervised approach},
	journal = {Journal of Mobile Multimedia},
	volume = {13},
	number = {3-4},
	year = {2017},
	note = {cited By 0},
	pages = {233-243},
	abstract = {Social media platforms have proven to be a powerful source of opinion sharing. Thus, mining and analyzing these opinions has an important role in decision-making and product benchmarking. However, the manual processing of the huge amount of content that these web-based applications host is an arduous task. This has led to the emergence of a new field of research known as Sentiment Analysis. In this respect, our objective in this work is to investigate sentiment classification in Arabic tweets using machine learning. Three classifiers namely Na{\"I}ve Bayes, Support Vector Machine and K-Nearest Neighbor were evaluated on an in-house developed dataset using different features. A comparison of these classifiers has revealed that Support Vector Machine outperforms others classifiers and achieves a 78\% accuracy rate. {\textcopyright} Rinton Press.},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040241035\&partnerID=40\&md5=76bc92b38d241173585a11bc74ae14d7},
	author = {Boudad, N. and Faizi, R. and Thami, R.O.H. and Chiheb, R.}
}
