This paper describes our system submitted to the DravidianLangTech@ACL 2026 shared task on Political Multiclass Sentiment Analysis of Tamil X (Twitter) Comments. The task requires classifying Tamil political tweets into seven sentiment categories. We address two key challenges, severe class imbalance and semantic overlap between categories, through a three-stage pipeline. First, we balance the training set by augmenting minority classes via back-translation and transformer-based paraphrasing. Second, we fine-tune XLM-RoBERTa-base using a class-weighted Focal Loss (𝛾=2), which directs learning towards hard, ambiguous samples. Third, we train five models under Stratified 5-Fold Cross-Validation and average their softmax outputs at inference time. On the official test set, the system achieves a Macro-F1 of 0.3539. The code is publicly available at: https://github.com/meclin2345/PolyTicsTamil_Alchemists

This paper describes our system submitted to the shared task on Hope Speech Detection in Tulu at DravidianLangTech@ACL 2026 hope-speech-dravidianlangtech-acl-2026. The task comprises two sub-tasks: coarse-grained classification into four categories Task 1 and fine-grained classification into five categories Task 2. We compare a traditional TF-IDF + LinearSVC baseline against XLM-RoBERTa fine-tuned with minority-class oversampling and Focal Loss. Our experiments reveal an interesting trade-off: while the transformer approach achieves the best validation Macro-F1 of 0.57 on the coarse-grained task, the TF-IDF baseline outperforms it on the smaller fine-grained task, highlighting the data scarcity threshold below which large pre-trained models struggle to generalise. On the official test set, our system achieves a Macro-F1 of 0.55 on Task 1 and 0.40 on Task 2. The code is publicly available at: https://github.com/meclin2345/Hope_Speech_Alchemists

This paper describes our system submitted to the shared task on Abusive Tamil Text Targeting Women on Social Media at DravidianLangTech@ACL 2026. We formulate the problem as a supervised binary classification task, assigning each Tamil social media comment to an Abusive or Non-Abusive category. Our pipeline begins with a tailored preprocessing stage that handles emoji translation, URL removal, and entity normalization. We then independently fine-tune two pre-trained transformer models MuRIL and XLM-RoBERTa on the task data. At inference time, we combine these models through a weighted softmax ensemble, assigning a weight of 0.6 to MuRIL and 0.4 to XLM-RoBERTa. The resulting system achieves a Macro-F1 score of 0.8115 on the test set, outperforming both individual models. The code is publicly available at: https://github.com/meclin2345/AbuseDetect_Alchemists