ABsTRACT. The rapid surge in social media usage has augmented the significance and value of data available on these platforms. As a result, analyzing community sentiment and opinions related to various topics and events using social media data has become increasingly crucial. However, the sheer volume of data produced on social media platforms surpasses human processing capabilities. Consequently, artificial intelligence-based models became frequently employed in social media analysis. In this study, deep learning (DL) and machine learning (ML) methods are applied to assess user opinions regarding airlines, and the effectiveness of these methods in social media analysis is comparatively discussed based on the performance results obtained. Due to the imbalanced nature of the dataset, synthetic data is produced using the Synthetic Minority Over-Sampling Technique (SMOTE) to enhance model performance. Before the SMOTE process, the dataset containing 14640 data points expanded to 27534 data points after the SMOTE process. The experimental results demonstrate that Support Vector Machines (SVM) achieved the highest performance among all methods with accuracy, precision, recall, and F-score values of 0.79 in the pre-SMOTE (imbalanced dataset). In contrast, Random Forest (RF) obtained the best performance among all methods, with accuracy, precision, recall, and F-score values of 0.88 in the post-SMOTE (balanced data set). Moreover, experimental findings demonstrate that SMOTE led to performance improvements in ML and DL models, ranging from a minimum of 3% to a maximum of 24% increase in F-Score metric.