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.
Highlights
The Coronavirus outbreak has been a severe disruption to the global economy and this has affected most, if not all, nations
Ever since the World Health organization (WHO) declared the pandemic a Public Emergency of International Concern (PEIC) [1], the subsequent restrictions to curb the spread of the virus continue to do more damage than good
A dataset of tweets about COVID-19 created by Christian et al [6] is selected for this work since it is the only dataset covering the period from January to October, and is considered big data
Summary
The Coronavirus outbreak has been a severe disruption to the global economy and this has affected most, if not all, nations. Social networking sites are considered the global big data center because people use their applications and invest much time in these media outlets [4]. Researchers contributed to an opensource of textual datasets To achieve this aim, a dataset of tweets about COVID-19 created by Christian et al [6] is selected for this work since it is the only dataset covering the period from January to October, and is considered big data. The contribution of this work is to analyze the COVID-19 tweets dataset from January to October and compare the changes in people‘s feelings by applying machine learning methods and answering the following two research questions: Question 1.
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