Sentiment Analysis of the Body-Shaming Beauty Vlog Comments

Abstract

Sentiment analysis can be used as a solution to identify body-shaming comments with the classification method using the Naïve Bayes Classifier algorithm. Naïve Bayes Classifier uses the concept of probability of each class in its classification learning. The purpose of this study is to predict or classify comment data based on shaming and non-shaming sentiment classes. The test in this study was carried out with ten different scenarios using the R programming language with RStudio tools which were then evaluated using the confusion matrix to determine the best classifier model. The evaluation results with the confusion matrix found that the best model classifier is a scenario with a comparison of training data and testing data 90:10 and applying to stem at the preprocessing. This scenario achieves an accuracy of 98.48% with an error rate of 1.52%. Recall is 99.53%, specificity is 66.67%, precision is 98.90%, and F-measure is 99.21%.