Sentiment Analysis of Nationwide Lockdown amid COVID 19: Evidence from Pakistan

ABSTRACT

A growing number of people are using tweets about the recent coronavirus epidemic of COVID-19 as a dataset to determine how worried people are in different parts of the world. This study attempts to uncover the key sentiments expressed by Twitter users regarding the COVID-19 epidemic by categorizing the tweets into positive and negative sentiments utilizing several resources (such as the Twitter search application programming interface (API), the Tweepy Python library, and the CSV excel database), as well as some predefined search terms ('#LockdownPakistan.'). We extracted the text of English language tweets from 28th March-1st May 2020. We have performed the sentiment analysis and classified the tweets in a binary class of positive and negative. Further, we used the word frequencies of single (unigrams), double (bigrams), and three words to examine the gathered tweets (tri-grams). According to our data, the majority of tweets express a positive attitude, with the word for lockdown COVID-19 appearing frequently. When looking at frequency analysis, the word 'family and time' stood out among the other words, which suggests that tweets were mostly optimistic and sentiments of defeating SARS-COV-2 prevail. People are determined to spend the lockdown in a good way. However, a few of the negative tweets, nevertheless, should serve as a warning for healthcare officials to make appropriate arrangements. Public health crisis responses are today complicated and highly synchronized both offline and online. Social media is a significant medium that gives people the chance to communicate with healthcare authorities directly. © 2022 IEEE.