000			02681cam a2200301 a 4500
003			EG-GiCUC
008			210915s2021 ua d f m 000 0 eng d
040			_aEG-GiCUC _beng _cEG-GiCUC
041	0		_aeng
049			_aDeposite
097			_aM.Sc
099			_aCai01.20.04.M.Sc.2021.Di.M
100	0		_aDina Farid Fouad
245	1	2	_aA model for enhancing cyber bullying detection for arabic language in social media / _cDina Farid Fouad ; Supervised Neamat Eltazi
246	1	5	_aنموذج لتحسين رصد التنمرالسيبرانى للغة العربية فى شبكات التواصل الإجتماعى
260			_aCairo : _bDina Farid Fouad , _c2021
300			_a72 Leaves : _bcharts ; _c30cm
502			_aThesis (M.Sc.) - Cairo University - Faculty of Computers and Artificial Intelligence - Department of Information Systems
520			_aThe social networking platform in Egypt is rapidly evolving, this rapid growth has made our lives easier and connected friends and families across the globe. As any tool, social networking is a double-edged weapon, and cyberbullying attacks have been proportionally increasing to this evolution.This research proposes an enhanced model to detect cyberbullying on social networks utilizing sentiment analysis for Arabic language. As there is a lack of existing research using Arabic sentiment analysis compared to English sentiment analysis, due to the nature and difficulty of the Arabic language, a deficiency in Arabic dataset used in sentiment analysis can be noticed. In the past few years, Arabic sentiment analysis has been put under a spotlight by the research community. However, most of the current published work focuses on English content, and not contributing much to the Arabic one. In this research we proposed an algorithm to detect cyberbullies on twitter using lexicon-based sentiment analysis specifically, Arabic tweets. The Proposed algorithm takes into consideration two main factors: the user history in terms of their past tweets, and the emoji{u2019}s included in the tweet. Our model starts with constructing our own dictionary for Egyptian dialect and then it includes three main stages which are Twitter Data Collection, Data Preprocessing and finally Data Classification and calculating the score.To confirm the feasibility of our model we ran a set of experiments and tested the model against multiple scenarios
530			_aIssued also as CD
653		4	_aArabic language
653		4	_aEnhancing cyber bullying
653		4	_aSocial media
700	0		_aNeamat Eltazi , _eSupervisor
905			_aNazla _eRevisor
905			_aShimaa _eCataloger
942			_2ddc _cTH
999			_c82141 _d82141