<![CDATA[Government subsidy policies are one of the important instruments inmaintaining economic stability and improving public welfare; however,they often generate diverse responses in the public sphere. These differingperspectives arise because subsidy policies directly affect the social andeconomic lives of the community. In the digital era, social mediaparticularly the Twitter platform has become a medium for the public toexpress opinions, criticisms, and information in real time regarding suchpolicies. This study aims to analyze public sentiment toward governmentsubsidy policies on the Twitter platform using the Naïve Bayes Classifiermethod with text preprocessing stages. The research data consist ofIndonesian-language tweets collected from the Twitter platform during aspecific period in 2024. The text preprocessing stages include case folding,tokenization, filtering/stopword removal, and stemming to eliminateirrelevant words before the sentiment classification process into positive,negative, and neutral categories. The results show that out of 87 analyzedtweets, neutral sentiment dominates with a percentage of 66.67%,followed by positive sentiment at 19.54% and negative sentiment at13.79%. The dominance of neutral sentiment indicates that most tweetsare informational in nature, while expressed opinions tend to be morepositive than negative. Overall, these findings demonstrate that the NaïveBayes Classifier method is able to provide an objective overview of publicsentiment trends toward government subsidy policies on the Twitterplatform and can be utilized as a references for policy evaluation.]]>
