<![CDATA[Public trust in PT Pertamina (Persero) is reflected in public opinion expressed on social media, particularly through the comment sections of news videos on YouTube. This study aims to analyze public sentiment toward Pertamina based on comments posted on YouTube news videos in order to identify the distribution of positive, negative, and neutral opinions. The data were collected using the YouTube Data API through a web crawling process and subsequently processed using several text preprocessing techniques, including cleaning, case folding, word normalization, tokenization, and stopword removal. Sentiment labeling was performed automatically using the VADER Sentiment method, which classified the comments into three categories: positive, negative, and neutral. Feature extraction was then conducted using the Term Frequency–Inverse Document Frequency (TF-IDF) method. The classification process compared the performance of three Naïve Bayes variants, namely Gaussian Naïve Bayes, Multinomial Naïve Bayes, and Bernoulli Naïve Bayes. Of the 56,868 comments that were successfully crawled, 22,527 comments were successfully collected. The sentiment labeling results revealed that neutral sentiment dominated the dataset, accounting for 95.89% of all comments, followed by positive sentiment at 2.86% and negative sentiment at 1.25%. The experimental results demonstrated that Multinomial Naïve Bayes achieved the best performance with an accuracy of approximately 95%, outperforming Bernoulli Naïve Bayes (approximately 92%) and Gaussian Naïve Bayes (approximately 79%). This superior performance is attributed to the compatibility of the Multinomial Naïve Bayes algorithm with TF-IDF feature representation, which is based on word frequency. The findings of this study are expected to provide valuable insights for Pertamina and policymakers in formulating more responsive and effective public communication strategies.]]>
