Sentiment Analysis using COVID-19 Twitter Data

ABSTRACT

The COVID-19 pandemic has essentially transformed the way millions of people across the world live their life. As offices remained closed for months, employees expressed conflicting sentiments on the work from home culture. People worldwide now use social media platforms such as Twitter to talk about their daily lives. This study aims to gage the public’s sentiment on working from home/remote locations during the COVID-19 pandemic by tracking their opinions on Twitter. It is essential to study these trends at this point in the pandemic as organizations should decide whether to continue remote work indefinitely or reopen offices and workspaces, depending on productivity, and employee satisfaction. Tweets posted in the live Twitter timeline is used to generate the set of data and accessed through Tweepy API. About 2 lakh tweets relevant to the remote work during the pandemic were tokenized and then passed to Naive Bayes classifier that classifies the sentiments positive, negative, neutral to every tweet. Our findings emphasize on population sentiment which is the effects of the COVID-19 pandemic, especially resulting from the work from home policy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.