A study on Covid-19 tweets

Abstract

The spread of infectious diseases has caused economic and human losses throughout history. The outbreak of the COVID-19 virus changed the lives of people around the world. Social media, such as Twitter, have played a significant role in providing the general public with the latest information and policy updates related to COVID-19 throughout the pandemic and government bodies [3]. Twitter is a platform for users to express their opinions and views. With the help of this microblogging platform, data can be extracted that can be used for sentiment and emotional analysis, a technique that is referred to as natural language processing (NLP). Over 14 months from 1 February 2020 to 31 March 2021, two billion multilingual tweets related to the COVID-19 pandemic were extracted and analyzed in a previous study [5]. This study will explore the data collection of tweets collected for COVID-19 from TBCOV [5]. The focus will be on the evolution of COVID-19 and understanding the trends of sentiments posted by Twitter users throughout South-East Asia, with a centering on Singapore, Thailand, Myanmar, and Vietnam during the COVID-19 pandemic. The results of the most indicative terms and named entities will be discussed using the Relative Entropy formula.