Analyzing sentiment dynamics from sparse text coronavirus disease-19 vaccination using natural language processing model

Abstract

Social media platforms enable people exchange their thoughts, reactions, emotions regarding all aspects of their lives. Therefore, sentiment analysis using textual data is widely practiced field. Due to large textual content available on social media, sentiment analysis is usually considered a text classification task. The high feature dimension is an important issue that needs to be resolved by examining text meaningfully. The proposed study considers a case study of coronavirus (COVID) vaccination to conclude public opinions about prospects for vaccination. Text corpus of tweets is collected, published between December 12, 2020, and July 13, 2021 is considered. The proposed model is developed considering phase-by-phase data analysis process, followed by an assessment of important information about the collected tweets on coronavirus disease (COVID-19) vaccine using two sentiment analyzer methods and probabilistic models for validation and knowledge analysis. The result indicated that public sentiment is more positive than negative. The study also presented statistics of trends in vaccination progress in the top countries from early 2021 to July 2021. The scope of study is enormous regarding sentiment analysis based on keyword and document modeling. The proposed work offers an effective mechanism for a decision-making system to understand public opinion and accordingly assists policymakers in health measures and vaccination campaigns.