Future of Information and Communication Conference (FICC) 2023
2-3 March 2023
Publication Links
IJACSA
Special Issues
Article Details
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Utilizing Deep Learning in Arabic Text Classification Sentiment Analysis of Twitter
Digital Object Identifier (DOI) : 10.14569/IJACSA.2022.0131297
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 12, 2022.
Abstract: The number of social media users has increased. These users share and reshare their ideas in posts and this information can be mined and used by decision-makers in different domains, who analyse and study user opinions on social media networks to improve the quality of products or study specific phenomena. During the COVID-19 pandemic, social media was used to make decisions to limit the spread of the disease using sentiment analysis. Substantial research on this topic has been done; however, there are limited Arabic textual resources on social media. This has resulted in fewer quality sentiment analyses on Arabic texts. This study proposes a model for Arabic sentiment analysis using a Twitter dataset and deep learning models with Arabic word embedding. It uses the supervised deep learning algorithms on the proposed dataset. The dataset contains 51,000 tweets, of which 8,820 are classified as positive, 37,360 neutral, and 8,820 as negative. After cleaning it will contain 31,413. The experiment has been carried out by applying the deep learning models, Convolutional Neural Network and Long Short-Term Memory while comparing the results of different machine learning techniques such as Naive Bayes and Support Vector Machine. The accuracy of the AraBERT model is 0.92% when applying the test on 3,505 tweets.
Keywords: Arabic sentiment analysis; machine learning; convolutional neural networks; word embedding; Arabic word2Vec; long short-term method; AraBERT
Nehad M. Ibrahim, Wael M. S. Yafooz, Abdel-Hamid M. Emara and Ahmed Abdel-Wahab, “Utilizing Deep Learning in Arabic Text Classification Sentiment Analysis of Twitter” International Journal of Advanced Computer Science and Applications(IJACSA), 13(12), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131297
@article{Ibrahim2022,
title = {Utilizing Deep Learning in Arabic Text Classification Sentiment Analysis of Twitter},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131297},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131297},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {12},
author = {Nehad M. Ibrahim and Wael M. S. Yafooz and Abdel-Hamid M. Emara and Ahmed Abdel-Wahab}
}
