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Body shaming is a form of mocking/insulting action by commenting on the shape or size of the body and the appearance of someone so that someone feels embarrassed. Problems with comments that contain body shaming elements are important things to study as text processing. 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, so that the distance between classes is not large. 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 confusion matrix to determine the best classifier model.. The evaluation results with confusion matrix found that the best model classifier is a scenario with a comparison of training data and testing data 90:10 and applying stemming 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%.