Covid-19 Public Opinion
A Twitter Healthcare Data Processing Using Machine Learning Methodologies
Bibliographic Data
| ID | 15480478 |
|---|---|
| Authors | Shweta Agrawal (0000-0002-0372-6128, Institute of Advanced Computing, SAGE University, Indore 452010, India, corresponding author), Sanjiv Jain (0000-0001-8942-7681, Medi-Caps University, corresponding author), Shruti Sharma (0000-0002-6039-8001, Indian Institute of Technology Indore), Ajay Khatri (0000-0002-6393-6952, Bellurbis Technologies Private Limited, Indore 452001, India) |
| Year | 2022 |
| Volume | 20 |
| Issue | 1 |
| Pages | 432-432 |
| Publication date | 2022-12-27 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph20010432 |
| PMID | 36612755 |
| OpenAlex | W4312223570 |
| Language | EN |
| Citations received | 1 |
| References cited | 30 |
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
Coronavirus disease 2019 (COVID-19 · Data science · Health care · Machine learning · Multinomial logistic regression · Naive Bayes classifier · Pandemic · Political science · Public opinion · Random forest · Sentiment analysis · Social media · Support vector machine · World Wide Web · Computer Science · Medicine · Misinformation and Its Impacts · Sentiment Analysis and Opinion Mining · Vaccine Coverage and Hesitancy · Artificial Intelligence
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Online Social Endorsement and Covid-19 Vaccine Hesitancy in the United Kingdom
| Unique citing works | 1 |
|---|---|
| Citations per year | 0.33 |
| Citation span | 2023 - 2023 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |