000			02750cam a2200313 a 4500
003			EG-GiCUC
008			180926s2018 ua d f m 000 0 eng d
040			_aEG-GiCUC _beng _cEG-GiCUC
041	0		_aeng
049			_aDeposite
097			_aM.Sc
099			_aCai01.18.02.M.Sc.2018.Am.P
100	0		_aAmal Mahmoud Mohammed
245	1	0	_aPredicting political sentiment from social network for Arabic slang / _cAmal Mahmoud Mohammed ; Supervised Hesham Hefny , Tarek Elghazaly
246	1	5	_aالتنبؤ بالتوجه السياسى بشبكات التواصل الاجتماعى وفقا للعربية العامية
260			_aCairo : _bAmal Mahmoud Mohammed , _c2018
300			_a140 Leaves : _bcharts ; _c30cm
502			_aThesis (M.Sc.) - Cairo University - Institute of Statistical Studies and Research - Department of Computer and Information Science
520			_aMicroblogs and social media platforms are now considered among the most popular forms of online communication. Through a platform like twitter, much information reflecting people{u2019}s opinions and attitudes is published and shared among users on a daily basis. This has recently brought great opportunities to companies interested in tracking and monitoring the reputation of their brands and businesses, and to policy makers and politicians to support their assessment of public opinions about their policies or political issues. In recent years, sentiment analysis on twitter turned into a recognized shared task challenge. Researchers related to this topic focus only on the English texts with very limited resources interested in Arabic texts, especially for the Egyptian dialect. This thesis discusses a proposed approach for political sentiment analysis for Arabic slang and provides classifier model for the purpose of obtaining information from the tweets. The case study used Twitter data associated with the (2012) presidential election in Egypt. We collected (17290) tweets and annotated them into positive and negative. The thesis also provides a comparison of different machine learning techniques applied to the case of political sentiment analysis in social media. Several machine learning methods were used during experimentation session: Naive Bayes, multinomial naive bayes, support vector machines, K-nearest neighbor and decisions tree and combining different classification algorithms bagging, random forest and stacking
530			_aIssued also as CD
653		4	_aArabic slang
653		4	_aPredicting political sentiment
653		4	_aSocial network
700	0		_aHesham Hefny , _eSupervisor
700	0		_aTarek Elghazaly , _eSupervisor
905			_aNazla _eRevisor
905			_aSamia _eCataloger
942			_2ddc _cTH
999			_c67664 _d67664