<![CDATA[Social media platforms such as Twitter have become crucial for analyzing political sentiment, particularly in contexts where public opinion shifts rapidly. This study proposes a hybrid classification model that combines Probabilistic Uncertain Linguistic Term Set (PULTS), Stepwise Weight Assessment Ratio Analysis (SWARA), and ELimination Et Choice Translating REality (ELECTRE-I). Using a dataset of 7,800 tweets collected between January and July 2024 covering five major political parties in Indonesia, the model classifies tweets into positive, negative, and neutral sentiments. To address class imbalance, Easy Data Augmentation (EDA) was applied, while Term Frequency–Inverse Document Frequency (TF-IDF) was used for feature extraction. The results show that the proposed model achieves 90% accuracy and an F1-score of 85%, outperforming baseline methods such as SVM (86.7%), Naïve Bayes (83.3%), Decision Tree (88%), and K-Means (76.7%). These improvements demonstrate that the integration of linguistic uncertainty with expert-driven feature weighting provides measurable advantages in political sentiment classification. Beyond performance, the study contributes theoretically by extending multi-criteria decision-making methods into sentiment analysis and by offering a more interpretable alternative to opaque machine learning models. Together, these findings highlight the practical value of explainable decision frameworks for political communication while advancing methodological approaches for analyzing sentiment under uncertainty.]]>
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