@article{(Open Science Index):https://publications.waset.org/pdf/10000687,
	  title     = {Survey on Arabic Sentiment Analysis in Twitter},
	  author    = {Sarah O. Alhumoud and  Mawaheb I. Altuwaijri and  Tarfa M. Albuhairi and  Wejdan M. Alohaideb},
	  country	= {},
	  institution	= {},
	  abstract     = {Large-scale data stream analysis has become one of
the important business and research priorities lately. Social networks
like Twitter and other micro-blogging platforms hold an enormous
amount of data that is large in volume, velocity and variety.
Extracting valuable information and trends out of these data would
aid in a better understanding and decision-making. Multiple analysis
techniques are deployed for English content. Moreover, one of the
languages that produce a large amount of data over social networks
and is least analyzed is the Arabic language. The proposed paper is a
survey on the research efforts to analyze the Arabic content in
Twitter focusing on the tools and methods used to extract the
sentiments for the Arabic content on Twitter.
},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {9},
	  number    = {1},
	  year      = {2015},
	  pages     = {364 - 368},
	  ee        = {https://publications.waset.org/pdf/10000687},
	  url   	= {https://publications.waset.org/vol/97},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 97, 2015},
	}