<![CDATA[The rapid development of artificial intelligence (AI) technology has driven increased public interaction with AI-based platforms, including Deepfake AI. One of the main challenges that arises is how to objectively assess public opinion, particularly on social media, which serves as a primary medium for expressing opinions. This study aims to compare the performance of two machine learning algorithms, namely Support Vector Machine (SVM) and Naïve Bayes (NB), in analyzing public sentiment toward Deepfake AI on the X social media platform. The research dataset consists of 7,774 tweets collected between October and November 2024. After preprocessing, 5,559 tweets were used, categorized into three sentiment classes: positive, negative, and neutral. Data imbalance was addressed using the Synthetic Minority Over-sampling Technique (SMOTE), with 80% of the data allocated for training and 20% for testing. The results show that before applying SMOTE, the SVM algorithm achieved the highest accuracy at 71%, while Naïve Bayes only reached 62%. After the application of SMOTE, the performance of both algorithms improved, with SVM achieving 77% accuracy and Naïve Bayes reaching 68%. Thus, SVM proved to be the best-performing algorithm in this study, both before and after SMOTE application, delivering more balanced results across sentiment classes. This research demonstrates that sentiment analysis based on machine learning can be utilized to understand public opinion toward AI platforms, while also providing valuable insights for developers to improve service quality and strengthen public trust.]]>
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