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%.

Fat Girls of All Kinds, Unite!--Sentiment Analysis on Comments to Influencers of Plus Size Women’s Clothing at Xiaohongshu

“…[17] , based on the automatic process of finding useful videos using sentiment analysis of user comments with natural language processing. They were using, e.g., the Nave Bayes algorithm and testing it for different scenarios, and comparing the ratio of training and testing data and the accuracy [19] . Event classification and prediction of YOUTUBE comments for comment polarity are also discussed [20] .…”

Analisis sentimen terhadap Body Shaming pada Twitter menggunakan Metode Naïve Bayes Classifier

“…Sentiment analysis merupakan sebuah metode yang digunakan untuk memahami, mengekstrak data opini, dan mengolah data tekstual secara otomatis untuk mendapatkan sebuah sentiment yang terkandung dalam sebuah opini [13], [14]. Pada penelitian yang dilakukan [15] yang berjudul "Analisis Sentimen dari Body Shaming Beauty Vlog Komentar" bahwa perbandingan data latih dan data uji 90:10. Skenario ini mencapai akurasi 98,48%, Recall 99,53%, presisi 98,90% dan f-measure 99,21%.…”