{"references": ["J. Gantz and D. Reinsel, \"Digital Universe Study: Extracting Value from Chaos,\" EMC2, June 2011. (Online). Available: Internet: http://www.emc.com/leadership/programs/digital-universe.htm (Accessed 6 Nov 2014).", "\"The 2011 IDC Digital Universe study sponsored by EMC,\" (Online). Available: http://www.emc.com/collateral/about/news/idc-emc-digital-universe-2011-infographic.pdf(Accessed 6 Nov 2014).", "S. Sagiroglu and a. D.Sinanc. \"Big data: A review,\" in Proc. CTS, 2013, pp. 42 \u2013 47.", "\"Social Media Usage in Middle East \u2013 Statistics and Trends (Infographic),\" Go-Gulf, 4 Jun 2013. (Online). Available: http://www.go-gulf.com/blog/social-media-middle-east (Accessed 6 Nov 2014).", "B. Liu. (2012, Apr 22). Sentiment Analysis and Opinion Mining, (1st edition). (On-line). Available: http://www.cs.uic.edu/~liub/FBS/SentimentAnalysis-and-OpinionMining.pdf (Des 22, 2014]).", "\"About,\" Twitter, (Online). Available: https://about.twitter.com/what-is-twitter. (Accessed 6 Nov 2014).", "J. Akaichi. \"Social Networks' Facebook' Statutes Updates Mining for Sentiment Classification,\" in Porc. SOCIALCOM, 2013, pp. 886 - 891.", "R. Khasawneh, H. Wahsheh, M. Al Kabi and I. Aismadi. \"Sentiment analysis of arabic social media content: a comparative study,\" in Porc. ICITST, 2013, pp. 101 - 106.", "M. Itani, B. A. U. B. L. Math. &Comput. Sci. Dept., L. Hamandi, R. Zantout and I. Elkabani. \"Classifying sentiment in arabic social networks: Na\u00efve search versus Na\u00efve bayes,\" in Porc. ACTEA, 2012, pp. 192 - 197.\n[10]\tA. Mountassir, M. 5. U. R. M. ALBIRONI Res. Team, H. Benbrahim and I. Berrada. \"Some methods to address the problem of unbalanced sentiment classification in an arabic context,\" in Porc. CIST, 2012, pp. 43 - 48.\n[11]\tM. Al-Kabi, Z. J. Zarqa Univ., N. Abdulla and M. Al-Ayyoub. \"An analytical study of Arabic sentiments: Maktoob case study,\" in Porc. ICITST, 2013, pp. 89 - 94.\n[12]\tJ. Varlack. \"What are Blogs?,\" MedNews Blog, 2 March 2009 . (Online). Available: http://www.mednet-tech.com/newsletter/blogs/what-are-blogs. (Accessed 6 Nov 2014).\n[13]\t A. Shoukry and a. A. Rafea. \"Sentence Level Arabic Sentiment Analysis,\" in Proc. CTS, 2012, pp. 546 \u2013 550.\n[14]\tN. Abdulla, N. Ahmed, M. Shehab and a. M. Al-Ayyoub. \"Arabic Sentiment Analysis: Lexicon-Based and Corpus-Based,\" in Proc. AEECT, 2013, pp. 1 \u2013 6.\n[15]\tS. Ahmed and G. Qadah. \"Key Issues in Conducting Sentiment Analysis on Arabic Social Media Text,\" in Porc. IIT, 2013, pp. 72 \u2013 77.\n[16]\tS. El-Beltagy and A. Ali. \"Open Issues in the Sentiment Analysis of Arabic,\" in Porc. IIT, 2013, pp. 215-220.\n[17]\tM. Abdul-Mageed, S. K\u00a8ubler and a. M. Diab. \"SAMAR: A System for Subjectivity and Sentiment Analysis of Arabic Social Media,\" in Proc. WASSA, 2012, pp. 19-28.\n[18]\tJ. Salamah and a. A. Elkhlifi. \"Microblogging Opinion Mining Approach for Kuwaiti Dialect,\" in Proc. ICCTIM, Dubai, 2014.\n[19]\tS. Al-Osaimi and a. K. Badruddin. \"Role of Emotion icons in Sentiment classification of Arabic Tweets,\" in Porc. MEDES '14, 2014, pp.167-171.\n[20]\tR. Duwairi, R. Marji, N. Sha'ban and S. Rushaidat. \"Sentiment Analysis in Arabic Tweets,\" in Porc. ICICS, 2014, pp. 1 - 6.\n[21]\tL. Albraheem and a. H. Al-Khalifa. \"Exploring the problems of Sentiment Analysis in Informal,\" in Proc. IIWAS '12, 2012, pp. 415-418."]}

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.