Political sentiment analysis in Tamil social media is challenging due to informal language, sarcasm, emoji-driven sentiment inversion, and severe class imbalance. This paper presents TamilEcho, our system submitted to the Shared Task on Political Multiclass Sentiment Analysis of Tamil X (Twitter) Comments at DravidianLangTech@ACL 2026. We propose a hybrid architecture that integrates contextual representations from XLM-RoBERTa with lexical TF-IDF features and explicit sarcasm-aware emoji features. Domain-specific hashtag expansion is incorporated to enrich political context. To address class imbalance, we apply inverse-frequency class weighting and label smoothing during training. Experimental results demonstrate that hybrid feature fusion significantly improves performance over transformer-only baselines. Our final system achieves a Macro-F1 score of 0.3559 on the official test set, securing Rank 10 among participating teams. The results highlight the effectiveness of combining semantic, lexical, and pragmatic cues for fine-grained political sentiment classification in Tamil.