Paradigm Shift of Online Education System Due to COVID-19 Pandemic: A Sentiment Analysis Using Machine Learning

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