Classification of Public Opinion on Online Learning Policies using Various Support Vector Machine’s Kernel

Abstract

The COVID-19 pandemic has resulted in significant changes in the education sector. The government issued a policy so that learning must be carried out online from home. This policy became a polemic for teachers and students so that pro and con opinions emerged on social media, especially Twitter. Sentiment analysis of public opinion is an interesting study. Standard classification algorithms such as k-Nearest neighbours, naïve bayes, decision tree, random forest, and support vector machine (SVM) can categorize these opinions in a short time with good accuracy. Many studies show that SVM is more accurate than all other classification methods. SVM works using kernels, including Linear, Polynomial and Radial Basis Functions (RBF) where each kernel requires different parameters. The linear kernel only requires one parameter, namely c (Cost). The RBF kernel requires 2 parameters, c and ɣ (gamma) while the Polynomial kernel uses 2 parameters, c and degrees. SVM does not have default values for these parameters and are based on experience and experimentation. The wider the range of parameters, the more likely the classifier obtains the optimal values. This study tries some parameters values of SVM kernels for text classification based on sentiment. Testing using 5-fold cross validation and confusion matrix show that SVM with a linear kernel provides the best performance with an accuracy of above 84%.