Abstract:
Online mode of education has been identified as the subtle solution to continue learning during the pandemic. However, the
accessibility to online platforms, suitable devices, and connections are not equal across the globe thus raising the question
of whether the opinion of the public in the South Asian region where the technology is not comparatively higher as in the
western world would be the same as that to the global perspective. This study involves the sentiment analysis of natural
language processing on recently tweeted data and concludes that the sentiment of the South Asian public remains positive as
online education is the most suitable approach to overcome the learning difficulties during a pandemic. The study performs
a ternary classification based on the polarity scores obtained from two robust lexicon-based sentiment analyzer tools namely
VADER and TextBlob and observes that 63.2% of the tweets were positive, 30.5% of the tweets were neutral and around
6.3% of them were negative. Finally, topic modeling was also performed using the Latent Dirichlet Allocation method to
gain insight into each of the classesOnline mode of education has been identified as the subtle solution to continue learning during the pandemic. However, the
accessibility to online platforms, suitable devices, and connections are not equal across the globe thus raising the question
of whether the opinion of the public in the South Asian region where the technology is not comparatively higher as in the
western world would be the same as that to the global perspective. This study involves the sentiment analysis of natural
language processing on recently tweeted data and concludes that the sentiment of the South Asian public remains positive as
online education is the most suitable approach to overcome the learning difficulties during a pandemic. The study performs
a ternary classification based on the polarity scores obtained from two robust lexicon-based sentiment analyzer tools namely
VADER and TextBlob and observes that 63.2% of the tweets were positive, 30.5% of the tweets were neutral and around
6.3% of them were negative. Finally, topic modeling was also performed using the Latent Dirichlet Allocation method to
gain insight into each of the classes
