B. Liu, Sentiment analysis and opinion miningSynthesis lectures on human language technologies 5, References 1. Statisticbrain Twitter Factswww.statisticbrain.com/Twitter-statistics, pp.1-167, 2012.

C. Internet and U. Report, https://www.cia.gov/library/publications/resources/the- world-factbook/rankorder

. Statisticbrain, T. Us, and . Facts, https://www.statista.com/statistics/274564/monthly- active-Twitter-users-in-the-united-states

A. Tumasjan, T. Oliver-sprenger, P. G. Sandner, and I. M. Welpe, Predicting elections with Twitter: What 140 characters reveal about political sentiment, ICWSM, vol.10, pp.178-185, 2010.

A. Jungherr, Tweets and votes, a special relationship, Proceedings of the 2nd workshop on Politics, elections and data, PLEAD '13, pp.5-14, 2013.
DOI : 10.1145/2508436.2508437

G. Avello, P. T. Daniel, E. Metaxas, and . Mustafaraj, Limits of electoral predictions using twitter, Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media. Association for the Advancement of Artificial Intelligence, 2011.

D. Gayo-avello, I Wanted to Predict Elections with Twitter and all I got was this Lousy Paper A Balanced Survey on Election Prediction using Twitter Data, 2012.

J. Digrazia, K. Mckelvey, J. Bollen, and F. Rojas, More tweets, more votes: Social media as a quantitative indicator of political behavior, PloS one, vol.8, issue.11, 2013.

F. Franch, UK election prediction with social media, Journal of Information Technology & Politics, vol.2, issue.10 1, pp.2010-57, 1080.

A. Ceron, L. Curini, S. M. Iacus, and G. Porro, Every tweet counts? How sentiment analysis of social media can improve our knowledge of citizens??? political preferences with an application to Italy and France, New Media & Society, vol.30, issue.4, pp.340-358, 2014.
DOI : 10.1007/s11127-007-9204-7

G. Caldarelli, A. Chessa, F. Pammolli, G. Pompa, M. Puliga et al., A Multi-Level Geographical Study of Italian Political Elections from Twitter Data, PLoS ONE, vol.22, issue.5, 2014.
DOI : 10.1371/journal.pone.0095809.s006

P. Burnap, R. Gibson, L. Sloan, R. Southern, and M. Williams, 140 characters to victory?: Using Twitter to predict the UK 2015 General Election, Electoral Studies, vol.41, pp.230-233, 2016.
DOI : 10.1016/j.electstud.2015.11.017

A. Tweetinvi, https://www.nuget.org/packages/TweetinviAPI

E. Frank, M. A. Hall, and I. H. Witten, The WEKA Workbench. Online Appendix for "Data Mining: Practical Machine Learning Tools and Techniques, 2016.

M. A. Hearst, T. Susan, E. Dumais, J. Osuna, B. Platt et al., Support vector machines, IEEE Intelligent Systems and their Applications, pp.18-28, 1998.
DOI : 10.1109/5254.708428

N. V. Petrova and C. H. Wu, Prediction of catalytic residues using Support Vector Machine with selected protein sequence and structural properties, BMC Bioinformatics, vol.7, issue.1, pp.312-322, 2006.
DOI : 10.1186/1471-2105-7-312

D. Kotzias, M. Denil, N. De-freitas, and P. Smyth, From Group to Individual Labels Using Deep Features, Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '15, pp.597-606
DOI : 10.1145/1273496.1273643

J. Mcauley and J. Leskovec, Hidden factors and hidden topics, Proceedings of the 7th ACM conference on Recommender systems, RecSys '13, pp.165-172, 2013.
DOI : 10.1145/2507157.2507163

A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng et al., Learning word vectors for sentiment analysis, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume, pp.142-150, 2011.