Perception Analysis: Pro- and Anti- Vaccine Classification with NLP and Machine Learning

Okpala, Izunna
Romera Rodriguez, Guillermo
Zheng, Weibing
Halse, Shane
Kropczynski, Jess
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Online discussion of the ensuing pandemic exemplifies the extent and complexity of information required to understand human perception. Social media has proven to be a viable medium for identifying actionable data and analyzing public perception. As health sectors all over the world battled to obtain accurate information regarding COVID-19, this research focused on gauging public perceptions of the vaccine. The public reception of the vaccine can be determined by public perception. This study explores how to use machine learning to understand human perceptions in the context of the COVID-19 vaccine. Natural Language Processing (NLP) was employed to detect pro- and anti-vaccine tweets, while two machine learning classification models were used to study the patterns derived from the analysis. The study analyzed people's perceptions of the vaccine by presenting the results from a geographic region, while learning patterns that are likely to be associated with pro- or anti-vaccine perceptions.
Data Analytics, Data Mining and Machine Learning for Social Media, covid-19 vaccine, machine learning, natural language processing, perception analysis
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