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Artificial Intelligence Based Sentence Level Sentiment Analysis of COVID-19

Sundas Rukhsar1, Mazhar Javed Awan1, Usman Naseem2, Dilovan Asaad Zebari3, Mazin Abed Mohammed4,*, Marwan Ali Albahar5, Mohammed Thanoon5, Amena Mahmoud6

1 Department of Software Engineering, University of Management and Technology, Lahore, 54770, Pakistan
2 School of Computer Science, The University of Sydney, Sydney, Australia
3 Department of Computer Science, College of Science, Nawroz University, Duhok, 42001, Kurdistan Region, Iraq
4 College of Computer Science and Information Technology, University of Anbar, Anbar, 31001, Iraq
5 Department of Computer Science, Umm Al Qura University, Mecca, 24211, Saudi Arabia
6 Computer Science Department, Faculty of Computers and Information, Kafrelsheikh University, Kafrelsheikh, Egypt

* Corresponding Author: Mazin Abed Mohammed. Email: email

(This article belongs to this Special Issue: Intelligent Telehealth Monitoring with Man-Computer Interface)

Computer Systems Science and Engineering 2023, 47(1), 791-807.


Web-blogging sites such as Twitter and Facebook are heavily influenced by emotions, sentiments, and data in the modern era. Twitter, a widely used microblogging site where individuals share their thoughts in the form of tweets, has become a major source for sentiment analysis. In recent years, there has been a significant increase in demand for sentiment analysis to identify and classify opinions or expressions in text or tweets. Opinions or expressions of people about a particular topic, situation, person, or product can be identified from sentences and divided into three categories: positive for good, negative for bad, and neutral for mixed or confusing opinions. The process of analyzing changes in sentiment and the combination of these categories is known as “sentiment analysis.” In this study, sentiment analysis was performed on a dataset of 90,000 tweets using both deep learning and machine learning methods. The deep learning-based model long-short-term memory (LSTM) performed better than machine learning approaches. Long short-term memory achieved 87% accuracy, and the support vector machine (SVM) classifier achieved slightly worse results than LSTM at 86%. The study also tested binary classes of positive and negative, where LSTM and SVM both achieved 90% accuracy.


Cite This Article

S. Rukhsar, M. J. Awan, U. Naseem, D. A. Zebari, M. A. Mohammed et al., "Artificial intelligence based sentence level sentiment analysis of covid-19," Computer Systems Science and Engineering, vol. 47, no.1, pp. 791–807, 2023.

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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