<![CDATA[Social media has become a space where many individuals express their opinions freely, including negative comments that may lead to hate speech. One group often targeted by such speech is Gojek drivers. This study aims to classify user comments on Facebook into two sentiment categories: positive and negative, with a primary focus on negative comments. The data was collected from public Facebook posts using the APIFY scraping tool. After the data was gathered, several preprocessing stages were carried out, including case folding, cleaning, tokenization, normalization, stopword removal, and stemming. The text data was then converted into numerical form using CountVectorizer. The classification algorithm used in this research is Naive Bayes with the MultinomialNB model, as the input data consists of word frequency. The results of the model evaluation show that this algorithm performs well in classifying negative comments, especially in identifying word patterns that commonly appear in hate speech directed toward Gojek drivers.]]>
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