Analyzing political sentiment in code-mixed Tamil-English presents significant challenges due to informal jargon, severe class imbalance, and distribution shifts. This paper describes our system for the Political Multiclass Sentiment Analysis shared task at DravidianLangTech@ACL 2026, which categorizes tweets into seven sentiment classes. Our approach leverages XLM-RoBERTa integrated with Low-Rank Adaptation (LoRA). To mitigate majority-class dominance, we combine random oversampling with automated hyperparameter optimization to improve macro-level balance within this Parameter-Efficient Fine-Tuning (PEFT) framework. Enhanced by targeted preprocessing—specifically emoji demojization and noise removal—our system helps preserve nuanced symbolic cues, achieving a macro-average F1-score of 0.3763 and securing Rank 2 on the shared task leaderboard.