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Sentiment Analysis for Arabic Social Media News Polarity

Adnan A. Hnaif1,*, Emran Kanan2, Tarek Kanan1

1 Al-Zaytoonah University of Jordan, Faculty of Science and Information Technology, Amman, 11733, Jordan
2 Amman Arab University, Faculty of Computing and Informatics, Amman, 11953, Jordan

* Corresponding Author: Adnan A. Hnaif. Email: email

(This article belongs to this Special Issue: Secure Big Data Analytics for Smart City)

Intelligent Automation & Soft Computing 2021, 28(1), 107-119.


In recent years, the use of social media has rapidly increased and developed significant influence on its users. In the study of the behavior, reactions, approval, and interactions of social media users, detecting the polarity (positive, negative, neutral) of news posts is of considerable importance. This proposed research aims to collect data from Arabic social media pages, with the posts comprising the main unit in the dataset, and to build a corpus of manually-processed data for training and testing. Applying Natural Language Processing to the data is crucial for the computer to understand and easily manipulate the data. Therefore, Stop-Word removal, Stemming, and Normalization are applied. Several classifiers, such as Support Vector Machine, Naïve Bayes, K-Nearest Neighbor, Random Frost, and Decision Tree are used to train the dataset, and their accuracy is determined by data testing. These two steps are carried out using the open-source WEKA tool. As a result, each post is categorized into three different classes: positive, negative, and neutral. This research concludes that among the classifiers, SVM reaches the highest level of accuracy with a percentage of 83% for the F1-measure.


Cite This Article

A. A. Hnaif, E. Kanan and T. Kanan, "Sentiment analysis for arabic social media news polarity," Intelligent Automation & Soft Computing, vol. 28, no.1, pp. 107–119, 2021.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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