This paper describes our submission to SemEval-2026 Task 9 (Subtask 1) on Spanish online polarisation detection. We investigate whether sentiment-adapted pretrained language models provide an advantage over general-purpose multilingual models for binary polarisation classification. Under a controlled training setup, we compare a base XLM-RoBERTa model, an emotion-adapted model, and a sentiment-adapted XLM-R model trained on Twitter data. To mitigate overfitting in the relatively small training dataset, we additionally apply back-translation as a data augmentation strategy. Experimental results show that the sentiment-adapted checkpoint consistently outperforms the alternative pretrained models under identical conditions. When combined with back-translation augmentation, the final system achieves a macro-averaged F1 score of 0.743 on the preliminary competition leaderboard. These findings suggest that prior adaptation to affective signals in social media can provide beneficial inductive bias for polarisation detection.