@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}, }