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Deep learning for arabic text sentiment analysis / Rana Mahmoud Kamel Abdelmoneim Kamel ; Supervised Mohamed Nafie , Karim Seddik

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Rana Mahmoud Kamel Abdelmoneim Kamel , 2019Description: 96 P. : charts , photographs ; 30cmOther title:
  • التعلم العميق فى تحليل المشاعر للنص العربى [Added title page title]
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  • Issued also as CD
Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Electronics and Communications Summary: Nowadays, people express their opinions, reviews on products, movies, hotels{u2026}etc publicly on the internet on social media platforms, blogs or forums. The number of Arabic speaking users on the internet has increased in the last decade and the research for analyzing the Arabic text has gained a lot of attention. In my work, deep learning techniques are used to classify Arabic tweets and reviews into two classes (positive/ negative) and three classes (positive/ negative/ neutral). Also, this work investigates whether deep learning can overcome ordinary machine learning algorithms and replace the effort of feature engineering in previous work. Finally, deep learning proved to have better results than machine learning techniques for most of the used datasets by using a data augmentation architecture
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Item type Current library Home library Call number Copy number Status Date due Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.08.M.Sc.2019.Ra.D (Browse shelf(Opens below)) Not for loan 01010110080282000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.08.M.Sc.2019.Ra.D (Browse shelf(Opens below)) 80282.CD Not for loan 01020110080282000

Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Electronics and Communications

Nowadays, people express their opinions, reviews on products, movies, hotels{u2026}etc publicly on the internet on social media platforms, blogs or forums. The number of Arabic speaking users on the internet has increased in the last decade and the research for analyzing the Arabic text has gained a lot of attention. In my work, deep learning techniques are used to classify Arabic tweets and reviews into two classes (positive/ negative) and three classes (positive/ negative/ neutral). Also, this work investigates whether deep learning can overcome ordinary machine learning algorithms and replace the effort of feature engineering in previous work. Finally, deep learning proved to have better results than machine learning techniques for most of the used datasets by using a data augmentation architecture

Issued also as CD

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