Public Sentiment Analysis of the Burning Sun Scandal Involving Seungri (BIGBANG) on Social Media X Using the Naïve Bayes Method

Abstract

This study aims to analyze public sentiment toward the Burning Sun Scandal involving Seungri by using data from the X social media platform. The dataset consists of 163 tweets collected through a scraping process and processed using text mining techniques, including case folding, cleaning, tokenizing, stopword removal, and stemming. Feature extraction was performed using the TF-IDF method, followed by sentiment classification using the Multinomial Naïve Bayes algorithm with an 80:20 split for training and testing data. The results indicate that public sentiment is predominantly negative. However, the classification model achieved a relatively low performance with an accuracy of 36%. This result is influenced by several factors, such as inconsistent labeling, class imbalance, and the limited size of the dataset. This study demonstrates that the Naïve Bayes method can be applied to sentiment analysis; however, further data processing optimization is required to improve model performance.