<![CDATA[The rapid development of information and communication technology has driven social media to become a primary platform for users to express opinions on various events, including prestigious football matches such as El Clásico between Barcelona and Real Madrid. The high level of interaction among Instagram users generates a large volume of comments with unstructured text characteristics and diverse sentiments, making automatic sentiment analysis necessary to understand public opinion trends. This study aims to analyze the sentiment of Instagram user comments related to the El Clásico match by comparing the Naive Bayes and Support Vector Machine (SVM) algorithms. The dataset consists of 1,526 comments with an imbalanced sentiment class distribution. The research stages include text preprocessing, term weighting using Term Frequency–Inverse Document Frequency (TF-IDF), and sentiment classification. The experimental results show that the SVM algorithm outperforms Naive Bayes, achieving an accuracy of 62.88% and a weighted F1-score of 0.62, while Naive Bayes achieves an accuracy of 59.53% and a weighted F1-score of 0.52. These results indicate that SVM is more effective in handling high-dimensional data and imbalanced class distributions in social media sentiment analysis.]]>
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