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 © 2022 IEEE.
ABSTRACT
Covid-19 is the word which is horrifying the world which has infringed socio-economic problems. The main purpose of this article is to build a model which predicts sentiments on Covid-19 reviews which has been taken from Coronavirus Reviews from Kaggle repository. It is originally known as Corona Virus Diseases effected in 2019 and has been declared as pandemic by WHO. With the increase in cases through out the globe people getting panic and worry about their health. Physically and mental state of the people is becoming comparatively proportional for this pandemic situation. So in this case there is a need of a classification model which is to be implemented based on various measures to protect the countries by interpreting the facts and info about this pandemic. So in this paper we performed sentimental analysis using a Novel Classifier (SADBM) named as Sentimental Data Base Miner algorithm to classify the reviews or reviews given by various people from all over the globe, and then we have validated the accuracy of the model which is compared with basic classifiers like Nave-Bayes and SVM where the Novel algorithm gave best accuracy when compared with the other 2 models i.e., 95%.