Topic based Sentiment Analysis for COVID-19 Tweets
| Autor: | Alanoud Alotaibi, Abeer Alabbas, Mashail Alsolamy, Manal Abdulaziz |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | International Journal of Advanced Computer Science and Applications. 12 |
| ISSN: | 2156-5570 2158-107X |
| DOI: | 10.14569/ijacsa.2021.0120172 |
| Popis: | 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 © 2021 All rights reserved |
| Databáze: | OpenAIRE |
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