<![CDATA[Background: The expansion of digital political communication in Indonesia has intensified the circulation of evaluative language, making accurate sentiment analysis essential for understanding public opinion and ideological polarization. Objective: This study examines the effectiveness of transformer-based models in classifying sentence-level sentiment in Indonesian political discourse and the impact of domain-specific fine-tuning. Method: A supervised experiment was conducted using annotated political texts from social media and online news, comparing transformer models with baseline classifiers. Results: Transformer-based models outperform traditional and recurrent approaches in accuracy and F1-scores, while domain-specific fine-tuning improves the detection of negative and neutral sentiments, especially in implicit and framed expressions; challenges remain in sarcasm, metaphor, and context-dependent meaning. Implication: These results underscore the need for context-aware and domain-adapted models to enhance sentiment analysis reliability in politically nuanced and low-resource settings. Novelty: This study proposes a linguistically informed, domain-adapted transformer framework that demonstrates how contextual modeling and fine-tuning improve sentiment interpretation in Indonesian political discourse.]]>
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