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Conference proceeding
Applying Machine Learning to Analyze Anti-Vaccination on Tweets
2021 IEEE International Conference on Big Data (Big Data), pp.4426-4430
IEEE International Conference on Big Data (Big Data) (Orlando, Florida, USA, 12/15/2021–12/18/2021)
12/15/2021
Web of Science ID: WOS:000800559504077
Abstract
Inspection of Anti-COVID vaccination tweets can be useful for many such analyses, and extraction of relevant information about opinion expressed on Twitter. This study proposes an analytical framework for analyzing tweets (COVID Vaccine, especially the Anti- COVID Vaccine) to identify and categorize fine-grained details about the COVID19 disaster such as affected individuals, public feelings towards the vaccine and reopening of business, polarity of public opinions on the vaccine and services provided, discussed topic changing over temporal dimension, and different clustering algorithms. In this project, we have analyzed COVID -Vaccine related tweets and Anti-Vaccine tweets, performed sentiment analysis and Topic modeling, and compared various models' behavior based on different configuration and training datasets. The result of this work will help policy makers and data scientists to identify the best approach for twitter sentiment analysis and topic modeling as well as providing feedback on people attitude and opinion on COVID-19 vaccine.
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Details
- Title
- Applying Machine Learning to Analyze Anti-Vaccination on Tweets
- Publication Details
- 2021 IEEE International Conference on Big Data (Big Data), pp.4426-4430
- Resource Type
- Conference proceeding
- Conference
- IEEE International Conference on Big Data (Big Data) (Orlando, Florida, USA, 12/15/2021–12/18/2021)
- Publisher
- Institute of Electrical Electronic Engineers
- Number of pages
- 5
- Grant note
- National Science Foundation (10.13039/100000001)
- Copyright
- © 2021, IEEE
- Identifiers
- WOS:000800559504077; 99380586995006600
- Academic Unit
- Cybersecurity and Information Technology; Hal Marcus College of Science and Engineering
- Language
- English