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DOI: 10.14569/IJACSA.2021.0120172
PDF

Topic based Sentiment Analysis for COVID-19 Tweets

Author 1: Manal Abdulaziz
Author 2: Alanoud Alotaibi
Author 3: Mashail Alsolamy
Author 4: Abeer Alabbas

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 1, 2021.

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Abstract: The incessant Coronavirus pandemic has had a detrimental impact on nations across the globe. The essence of this research is to demystify the social media’s sentiments regarding Coronavirus. The paper specifically focuses on twitter and extracts the most discussed topics during and after the first wave of the Coronavirus pandemic. The extraction was based on a dataset of English tweets pertinent to COVID-19. The research study focuses on two main periods with the first period starting from March 01,2020 to April 30, 2020 and the second period starting from September 01,2020 to October 31, 2020. The Latent Dirichlet Allocation (LDA) was adopted for topics extraction whereas a lexicon based approach was adopted for sentiment analysis. In regards to implementation, the paper utilized spark platform with Python to enhance speed and efficiency of analyzing and processing large-scale social data. The research findings revealed the appearance of conflicting topics throughout the two Coronavirus pandemic periods. Besides, the expectations and interests of all individuals regarding the various topics were well represented.

Keywords: Social media analysis; COVID-19; topics extraction; sentiment analysis; LDA; spark; twitter

Manal Abdulaziz, Alanoud Alotaibi, Mashail Alsolamy and Abeer Alabbas, “Topic based Sentiment Analysis for COVID-19 Tweets” International Journal of Advanced Computer Science and Applications(IJACSA), 12(1), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120172

@article{Abdulaziz2021,
title = {Topic based Sentiment Analysis for COVID-19 Tweets},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120172},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120172},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {1},
author = {Manal Abdulaziz and Alanoud Alotaibi and Mashail Alsolamy and Abeer Alabbas}
}



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.

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