Search for Articles:

MECS Press Menu

El-Sayed M.El-Horbaty

Work place: Computer Science Department, Faculty of computer and information sciences, Ain Shams University, Cairo, Egypt

E-mail: shorbaty@cis.asu.edu.eg

Website:

Research Interests:

Biography

El-Sayed M. El-Horbaty, He received his Ph.D. in Computer science from London University, U.K (1985)., his M.Sc. (1978) and B.Sc (1974) in Mathematics From Ain Shams University, Egypt. His work experience includes 44 years as an in Egypt (Ain Shams University), Qatar(Qatar University) and Emirates (Emirates University, Ajman University and ADU University). He worked as Deputy Dean of the faculty of IT, Ajman University (2002-2008). He is working as a Vice Dean of the faculty of Computer & Information Sciences, Ain Shams University (2010-2017). Prof. El-Horbaty is current areas of research are parallel and distributed computing, combinatorial optimization, image processing, cloud computing, e-health and mobile cloud computing. His work appeared in journals such as Parallel Computing, International Journal of Mobile Network Design and Innovation, International Journal of bio-Medical Informatics and e-health, and International journal of Computers and Applications (IJCA), Applied Mathematics and Computation, and International Review on Computers and software. Also he has been involved in more than 26 conferences.

Author Articles
Twitter Benchmark Dataset for Arabic Sentiment Analysis

By Donia Gamal Marco Alfonse El-Sayed M.El-Horbaty Abdel-Badeeh M.Salem

DOI: https://doi.org/10.5815/ijmecs.2019.01.04, Pub. Date: 8 Jan. 2019

Sentiment classification is the most rising research areas of sentiment analysis and text mining, especially with the massive amount of opinions available on social media. Recent results and efforts have demonstrated that there is no single strategy can mutually accomplish the best prediction performance on various datasets. There is a lack of existing researches to Arabic sentiment analysis compared to English sentiment analysis, because of the unique nature and difficulty of the Arabic language which leads to shortage in Arabic dataset used in sentiment analysis. An Arabic benchmark dataset is proposed in this paper for sentiment analysis showing the gathering methodology of the most recent tweets in different Arabic dialects. This dataset includes more than 151,000 different opinions in variant Arabic dialects which labeled into two balanced classes, namely, positive and negative. Different machine learning algorithms are applied on this dataset including the ridge regression which gives the highest accuracy of 99.90%.

[...] Read more.
Other Articles

Subscribe to receive issue release notifications and newsletters from MECS Press journals