@article {815,
	title = {Sentiment Analysis of Arabic Tweets: Opinion Target Extraction-},
	journal = {Journal of Digital Information Management},
	volume = {16},
	year = {2018},
	month = {12/2018},
	pages = {324-331},
	type = {Research},
	chapter = {324},
	abstract = {<p>\&nbsp;Due to the increased volume of Arabic opinionated posts on different social media, Arabic sentiment analysis is viewed as an important research field. Identifying the target or the topic on which opinion has been expressed is the aim of this work. Opinion target identification is a problem that was generally very little treated in Arabic text. In this paper, an opinion target extraction method from Arabic tweets is proposed. First, as a preprocessing phase, several feature forms from tweets are extracted to be examined. The aim of these forms is to evaluate their impacts on accuracy. Then, two classifiers, SVM and Na{\"\i}ve Bayes are trained. The experiment results show that, with 500 tweets collected and manually tagged, SVM gives the highest precision and recall (86\%).</p>
},
	doi = {https://doi.org/10.6025/jdim/2018/6/324-331},
	url = {http://dline.info/fpaper/jdim/v16i6/jdimv16i6_4.pdf},
	author = {Salima BEHDENNA and Fatiha Barigou and Ghalem Belalem}
}
@article {704,
	title = {Sentiment Analysis of Arabic Tweets: Opinion Target Extraction},
	journal = {Journal of Digital Information Management},
	volume = {16},
	year = {2018},
	month = {12/2018},
	pages = {324-331},
	type = {Research},
	chapter = {324},
	doi = {https://doi.org/10.6025/jdim/2018/6/324-331},
	url = {http://dline.info/fpaper/jdim/v16i6/jdimv16i6_4.pdf},
	author = {Salima BEHDENNA and Fatiha Barigou and Ghalem Belalem}
}
