A Novel Classification Technique for Safety Measures on Covid-19 Using Featured-Based Sentimental Analysis

Abstract

Covid-19 is a term that has frightened the globe because it has broken beyond socioeconomic barriers in which people literally forgot the word social help because of this deadliest virus.The main goal of this study is to create a model that forecasts Covid-19 reviews based on coronavirus ratings from Kaggle repository. The World Health Organization(WHO) declared a pandemic of the coronavirus infection when it first appeared in 2019. People are worrying and concerned about their health as the number of instances rises throughout the world. People’s physical and emotional health is inversely proportional to the pandemic scenario. As a result, in this case, a categorization model based on numerous metrics is required to rescue nations by analyzing facts and information about the outbreak. In this article to organise the reviews or opinions provided by people worldwide, we performed emotional or opinion classification using a Novel classifier. Then, the accuracy of the proposed model is compared with existing base classifiers like NB(Naive-Bayes) and Support Vector Machine(SVM), where Novel classifier gave the best accuracy compared to the other two classifiers, i.e., 95