Volume 12, Number 3

Twitter based Sentiment Analysis of Impact of Covid-19 on Education Globaly


Swetha Sree Cheeti, Yanyan Li and Ahmad Hadaegh, California State University-San Marcos, USA


Education system has been gravely affected due to widespread of Covid-19 across the globe. In this paper we present a thorough sentiment analysis of tweets related to education available on twitter platform and deduce conclusions about its impact on people’s emotions as the pandemic advanced over the months. Through twitter over ninety thousand tweets have been gathered related to the circumstances involving the change in education system over the world. Using Natural language tool kit (NLTK) functionalities and Naive Bayes Classifier a sentiment analysis has been performed on the gathered dataset. Based on the results of this analysis we infer to exhibit the impact of covid-19 on education and how people’s sentiment altered due to the changes with regard to the education system. Thus, we would like to present a better understanding of people’s sentiment on education while trying to cope with the pandemic in such unprecedented times.


Sentiment Analysis, Education, Covid-19, Tweets, Naïve Bayes Classifier.