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.
[1] 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).
[2] "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).
[3] S. Sagiroglu and a. D.Sinanc. "Big data: A review," in Proc. CTS, 2013, pp. 42 – 47.
[4] "Social Media Usage in Middle East – 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).
[5] 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]).
[6] "About," Twitter, (Online). Available: https://about.twitter.com/what-is-twitter. (Accessed 6 Nov 2014).
[7] J. Akaichi. "Social Networks' Facebook' Statutes Updates Mining for Sentiment Classification," in Porc. SOCIALCOM, 2013, pp. 886 - 891.
[8] 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.
[9] M. Itani, B. A. U. B. L. Math. &Comput. Sci. Dept., L. Hamandi, R. Zantout and I. Elkabani. "Classifying sentiment in arabic social networks: Naïve search versus Naïve bayes," in Porc. ACTEA, 2012, pp. 192 - 197.
[10] A. 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.
[11] M. 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.
[12] J. 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).
[13] A. Shoukry and a. A. Rafea. "Sentence Level Arabic Sentiment Analysis," in Proc. CTS, 2012, pp. 546 – 550.
[14] N. Abdulla, N. Ahmed, M. Shehab and a. M. Al-Ayyoub. "Arabic Sentiment Analysis: Lexicon-Based and Corpus-Based," in Proc. AEECT, 2013, pp. 1 – 6.
[15] S. Ahmed and G. Qadah. "Key Issues in Conducting Sentiment Analysis on Arabic Social Media Text," in Porc. IIT, 2013, pp. 72 – 77.
[16] S. El-Beltagy and A. Ali. "Open Issues in the Sentiment Analysis of Arabic," in Porc. IIT, 2013, pp. 215-220.
[17] M. Abdul-Mageed, S. K¨ubler and a. M. Diab. "SAMAR: A System for Subjectivity and Sentiment Analysis of Arabic Social Media," in Proc. WASSA, 2012, pp. 19-28.
[18] J. Salamah and a. A. Elkhlifi. "Microblogging Opinion Mining Approach for Kuwaiti Dialect," in Proc. ICCTIM, Dubai, 2014.
[19] S. Al-Osaimi and a. K. Badruddin. "Role of Emotion icons in Sentiment classification of Arabic Tweets," in Porc. MEDES '14, 2014, pp.167-171.
[20] R. Duwairi, R. Marji, N. Sha'ban and S. Rushaidat. "Sentiment Analysis in Arabic Tweets," in Porc. ICICS, 2014, pp. 1 - 6.
[21] L. Albraheem and a. H. Al-Khalifa. "Exploring the problems of Sentiment Analysis in Informal," in Proc. IIWAS '12, 2012, pp. 415-418.
[1] 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).
[2] "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).
[3] S. Sagiroglu and a. D.Sinanc. "Big data: A review," in Proc. CTS, 2013, pp. 42 – 47.
[4] "Social Media Usage in Middle East – 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).
[5] 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]).
[6] "About," Twitter, (Online). Available: https://about.twitter.com/what-is-twitter. (Accessed 6 Nov 2014).
[7] J. Akaichi. "Social Networks' Facebook' Statutes Updates Mining for Sentiment Classification," in Porc. SOCIALCOM, 2013, pp. 886 - 891.
[8] 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.
[9] M. Itani, B. A. U. B. L. Math. &Comput. Sci. Dept., L. Hamandi, R. Zantout and I. Elkabani. "Classifying sentiment in arabic social networks: Naïve search versus Naïve bayes," in Porc. ACTEA, 2012, pp. 192 - 197.
[10] A. 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.
[11] M. 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.
[12] J. 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).
[13] A. Shoukry and a. A. Rafea. "Sentence Level Arabic Sentiment Analysis," in Proc. CTS, 2012, pp. 546 – 550.
[14] N. Abdulla, N. Ahmed, M. Shehab and a. M. Al-Ayyoub. "Arabic Sentiment Analysis: Lexicon-Based and Corpus-Based," in Proc. AEECT, 2013, pp. 1 – 6.
[15] S. Ahmed and G. Qadah. "Key Issues in Conducting Sentiment Analysis on Arabic Social Media Text," in Porc. IIT, 2013, pp. 72 – 77.
[16] S. El-Beltagy and A. Ali. "Open Issues in the Sentiment Analysis of Arabic," in Porc. IIT, 2013, pp. 215-220.
[17] M. Abdul-Mageed, S. K¨ubler and a. M. Diab. "SAMAR: A System for Subjectivity and Sentiment Analysis of Arabic Social Media," in Proc. WASSA, 2012, pp. 19-28.
[18] J. Salamah and a. A. Elkhlifi. "Microblogging Opinion Mining Approach for Kuwaiti Dialect," in Proc. ICCTIM, Dubai, 2014.
[19] S. Al-Osaimi and a. K. Badruddin. "Role of Emotion icons in Sentiment classification of Arabic Tweets," in Porc. MEDES '14, 2014, pp.167-171.
[20] R. Duwairi, R. Marji, N. Sha'ban and S. Rushaidat. "Sentiment Analysis in Arabic Tweets," in Porc. ICICS, 2014, pp. 1 - 6.
[21] L. Albraheem and a. H. Al-Khalifa. "Exploring the problems of Sentiment Analysis in Informal," in Proc. IIWAS '12, 2012, pp. 415-418.
@article{"International Journal of Business, Human and Social Sciences:69220", author = "Sarah O. Alhumoud and Mawaheb I. Altuwaijri and Tarfa M. Albuhairi and Wejdan M. Alohaideb", title = "Survey on Arabic Sentiment Analysis in Twitter", 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.
", keywords = "Big Data, Social Networks, Sentiment Analysis.", volume = "9", number = "1", pages = "364-5", }