Polarization detection in short texts is a challenging and relevant problem in Natural Language Processing, particularly in social media environments where regional variationsand subtle discursive nuances converge. Inthis paper, we describe our participation inSubtask 1 (Spanish) of SemEval-2026 Task 9(Naseem et al., 2026a), which focuses on binary polarization classification. We evaluatetwo main strategies: lexical models based onBag-of-Words representations and regionallypre-trained Transformer models for Spanish. Inaddition, we explore a logistic stacking framework that combines lexical and contextual representations. Our experiments show that regionally adapted Transformers generally outperform purely lexical approaches, with BILMALATachieving the strongest performance in this task.The results highlight the importance of regionally aligned pre-training on social media datafor effective polarization detection in Spanish.