Sentiment Classification and Comparison of Covid-19 Tweets During the First Wave and the Second Wave Using NLP Techniques and Libraries

ABSTRACT

This research focuses on analysing the sentiments of people pertaining to severe periodic outbreaks of COVID-19 on two junctures – First Wave (Mar’20 & Apr’20) and Second Wave (Jun’21 & Jul’21)-since the first lockdown was undertaken with a view to curb the vicious spread of the lethal SARS-Cov-2 strain. Primarily, the objective is to analyse the public sentiment – as evident in the posted tweets - relating to the different phases of the pandemic, and to illuminate how keeping an eye on change in the tenor and tone of discussions can help government authorities and healthcare industry in raising awareness, reducing panic amongst citizens, and planning strategies to tackle the monumental crisis. Considering the daily volume of social media activity, in our project, we scoped to analyse the Tweets related to the two different pandemic stages – The First wave and the Second wave – by implementing Text Mining and Sentiment Analysis, subfields of Natural Language Processing. To manually extract tweets from the platform, we used Twitter API coupled with Python’s open-source package using a set of COVID-19-related keywords. Crucially, before finalising the project pipeline, we conducted a thorough secondary research to find the solutions and methodologies implemented in our area of interest. We listed the current works and attempted to plug the gaps in those via our experiment. We used several classification and boosting algorithms to create a framework to distinguish the textual data of the tweets. Relevant scope, limitations, and room for improvements have been discussed comprehensively in the upcoming sections. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.