COVID-19 Public Opinion: A Twitter Healthcare Data Processing Using Machine Learning Methodologies

Shweta Agrawal (), Sanjiv Kumar Jain (), Shruti Sharma and Ajay Khatri
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Shweta Agrawal: Institute of Advanced Computing, SAGE University, Indore 452010, India
Sanjiv Kumar Jain: Electrical Engineering Department, Medi-Caps University, Indore 453331, India
Shruti Sharma: Department of Computer Science and Engineering, Indore Institute of Science &Technology, Indore 453332, India
Ajay Khatri: Bellurbis Technologies Private Limited, Indore 452001, India

IJERPH, 2022, vol. 20, issue 1, 1-17

Abstract: The COVID-19 pandemic has shattered the whole world, and due to this, millions of people have posted their sentiments toward the pandemic on different social media platforms. This resulted in a huge information flow on social media and attracted many research studies aimed at extracting useful information to understand the sentiments. This paper analyses data imported from the Twitter API for the healthcare sector, emphasizing sub-domains, such as vaccines, post-COVID-19 health issues and healthcare service providers. The main objective of this research is to analyze machine learning models for classifying the sentiments of people and analyzing the direction of polarity by considering the views of the majority of people. The inferences drawn from this analysis may be useful for concerned authorities as they work to make appropriate policy decisions and strategic decisions. Various machine learning models were developed to extract the actual emotions, and results show that the support vector machine model outperforms with an average accuracy of 82.67% compared with the logistic regression, random forest, multinomial naïve Bayes and long short-term memory models, which present 78%, 77%, 68.67% and 75% accuracy, respectively.

Keywords: machine learning; polarity; sentiment analysis; classification; accuracy; healthcare; social media; artificial intelligence (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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