The rapid growth of social media networks faces challenges in the classification of multilingual and code-mixed data. A task is shared by Political Multiclass Sentiment Analysis of Tamil X (Twitter) -DravidianLangTech@ACL 2026 to classify the political text.For the above task, we proposed solutions to compare a traditional machine learning and the transformer based model. First we developed a Baseline traditional Support vector Machine model using the TF-IDF features. To provide a stronger Indic-language baseline we consider the IndicBERT, a transformer model specifically designed for Indian Languages. IndicBERT improves contextual understanding of Tamil-English code-mixed political text . To capture the deeper information from the text we developed an XLM-RoBERTa model where we used minimal pre-processing technique. The Result shows us that the transformer-based performs well compared to the traditional baseline model with the macro F1 score of 0.3738. The Study highlights the importance of robust multi-class social media political text classification.