Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification

Version 1 : Received: 1 May 2020 / Approved: 2 May 2020 / Online: 2 May 2020 (13:52:28 CEST)

A peer-reviewed article of this Preprint also exists.

Samuel, J.; Ali, G.G.M.N.; Rahman, M.M.; Esawi, E.; Samuel, Y. COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification. Information 2020, 11, 314. Samuel, J.; Ali, G.G.M.N.; Rahman, M.M.; Esawi, E.; Samuel, Y. COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification. Information 2020, 11, 314.

Journal reference: Information 2020, 11
DOI: 10.3390/info11060314

Abstract

Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fuelled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19's informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91% for short Tweets, with the Naive Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities.

Subject Areas

COVID-19; coronavirus; machine learning; sentiment analysis; textual analytics; Twitter

Comments (1)

Comment 1
Received: 11 June 2020
Commenter: Md Mokhlesur Rahman
The commenter has declared there is no conflict of interests.
Comment: The peer-reviewed version of this paper has been accepted and published online. You can find the paper at the following link. https://www.mdpi.com/2078-2489/11/6/314/htm
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