Abstract
The COVID-19 epidemic has completely altered the environment and every
aspect of every individual. The most affected part is the education system and the
stakeholders associated with it. Organizations are currently being forced to adapt and
alter their strategies in response to the new situation created by the COVID-19
epidemic. The proposed study gathers tweets on online schooling from social media
sites like Twitter and Facebook comments in order to conduct a thorough sentiment
analysis (SA) during the epidemic. The current study utilizes techniques for natural
language processing (NLP) and machine learning (ML) to extract subjective data,
establish polarity, and identify how people felt about the educational system prior to
and following the COVID-19 crisis. The first step in the proposed study is to retrieve
tweets using Twitter APIs before they are ready for rigorous preprocessing. One
filtering method is Information Gain (IG). We will identify and examine the latent
causes of the unpleasant feelings. We'll look at the machine-learning classification
algorithm at the end. The proposed model will analyse the perceptions of people about
the online educational system during COVID-19