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Applying Machine Learning to Analyze Anti-Vaccination on Tweets
Conference proceeding   Peer reviewed

Applying Machine Learning to Analyze Anti-Vaccination on Tweets

Maryam Taeb, Hongmei Chi and Jie Yan
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
DOI: https://doi.org/10.1109/BigData52589.2021.9671647
Web of Science ID: WOS:000800559504077

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14 Record Views
4 Times Cited - Web of Science

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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