SENTIMENT CLASSIFICATION AND ANALYSIS ON DISTANCE LEARNING

Abstract

The aim of this report is to study the reaction of people of the world towards distance education or distance learning. Females, vocational high school graduates and full time working students agree with some of the statements more than others do because for them it is convenient to do courses online. It is thought that students are satisfied with this education system which provides great convenience in time and cost. In these changing times, globalization hales the education systems to have different alternatives of education, and one of them is Distance Education. Today distance education is an interesting option for those people who lack the opportunity to attend the traditional system of education -face to-face - due to constraints in time, space, and money that the students might have. What and how the students learn is the main concern which can take us to establish what is to be evaluated and how to evaluate considering that DE could be for everybody no matter the background, self-confidence and intellectual preferences of the interested students. Online education comes in shades of grey. We analyze public view on the pandemic in regards with mental stress, interrupted power supply, affordability and access to internet, flexibility of schedule, reduction of long-distance commute, risk of covid and other such consequences of online learning and Government’s take on the need for inclusive education policies. In this research, we qualitatively inspect the consequences of COVID-19 pandemic on education of the students. This study primarily focuses on the response of students of all age groups, educators, college professors, schoolteachers and also parents of young students towards the approach of distance learning or Online education in the past two years. We have taken two datasets, first being the Twitter dataset comprising of tweets from around the whole world and second, dataset which is specific to tweets from India. The data has been extracted from twitter with the aid of twitter API and then two sentiment analysis approaches have been implemented, first Machine learning classifiers namely, Naïve Bayes, SVM, VI Random Forest, Logistic Regression, KNN, XG-Boost and secondly, Lexicon Based algorithms, VADER and TEXTBLOB. Upon performing the said approaches, the maximum accuracy achieved is 94%. This study seeks to examine the extent to which the community accepts distance learning as a precaution by employing the sentiment analysis of Twitter’s tweets as one of the most popular social media. This method is considered effective due to its ability to access the community’s tweets quickly and at a low cost.