Hossam Elzayady

Work place: Military technical college; /Department of computer engineering, Cairo, Egypt

E-mail: hossamelzaiade@gmail.com


Research Interests: Artificial Intelligence, Computational Learning Theory, Data Structures and Algorithms


Hossam Elzayady is a Ph.D. candidate at the Department of computer engineering, received a Bachelor Degree in computer engineering and Masters of Science degree from the MTC, Cairo, Egypt, in 2005 and 2018, respectively. His research interests are in artificial intelligent, data science, machine learning.

Author Articles
Arabic Opinion Mining Using Combined CNN - LSTM Models

By Hossam Elzayady Khaled M. Badran Gouda I. Salama

DOI: https://doi.org/10.5815/ijisa.2020.04.03, Pub. Date: 8 Aug. 2020

In the last few years, Sentiment Analysis regarding customers' reviews in order to comprehend the opinion polarity on social media has received considerable attention. However, the improvement of deep learning for sentiment analysis relating to customer reviews in Arabic language has received less attention. In fact, many users post and jot down their reviews in Arabic daily, so we ought to shed more light on Arabic sentiment analysis. Most likely all previous work depends on conventional classification techniques, such as KNN, Naïve Bayes (NB), etc. But in this work, we implement two deep learning models: Long Short Term Memory (LSTM) and Convolution Neural Networks (CNN), in addition to three traditional techniques: Naïve Bayes, K-Nearest Neighbor (KNN), Decision trees for sentiment analysis and compared the experimental results. Also, we offer a combined model from CNN and Recurrent Neural Network (RNN) architecture where this model collects local features through CNN as the input for RNN for Arabic sentiment analysis of short texts. An appropriate data preparation has been conducted for each utilized dataset. Our Conducted experiments for each dataset against traditional machine learning classifier; KNN, NB, and decision trees and regular deep learning models; CNN and LSTM, has resulted in impressive performance using our proposed combined (CNN-LSTM) model with an average accuracy of 85,83%, 86,88% for HTL and LABR datasets respectively.

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