@inproceedings{kumari-etal-2026-polyticstamil,
title = "{P}oly{T}ics{T}amil{\_}{A}lchemists@{D}ravidian{L}ang{T}ech@{ACL} 2026: An Augmentation-Driven Focal Ensemble Model for Political Sentiment Analysis in {T}amil",
author = "Kumari, Jyoti and
Francis, Meclin A and
Ulli, Vinay Babu and
Sreekumar, Malavika and
Johnson, Joel",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Thavareesan, Sajeetha and
Rajiakodi, Saranya and
Navaneethakrishnan, Subalalitha and
Chinnappa, Dhivya and
Palani, Balasubramanian and
Subramanian, Malliga and
Shanmugavadivel, Kogilavani and
Rajalakshmi, Ratnavel",
booktitle = "Proceedings of the Sixth Workshop on Speech, Vision, and Language Technologies for {D}ravidian Languages",
month = jul,
year = "2026",
address = "Underline (Virtual)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.dravidianlangtech-1.50/",
pages = "326--330",
ISBN = "979-8-89176-401-9",
abstract = "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 ($\gamma{=}2$), 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"
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<abstract>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</abstract>
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%0 Conference Proceedings
%T PolyTicsTamil_Alchemists@DravidianLangTech@ACL 2026: An Augmentation-Driven Focal Ensemble Model for Political Sentiment Analysis in Tamil
%A Kumari, Jyoti
%A Francis, Meclin A.
%A Ulli, Vinay Babu
%A Sreekumar, Malavika
%A Johnson, Joel
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Rajiakodi, Saranya
%Y Navaneethakrishnan, Subalalitha
%Y Chinnappa, Dhivya
%Y Palani, Balasubramanian
%Y Subramanian, Malliga
%Y Shanmugavadivel, Kogilavani
%Y Rajalakshmi, Ratnavel
%S Proceedings of the Sixth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
%D 2026
%8 July
%I Association for Computational Linguistics
%C Underline (Virtual)
%@ 979-8-89176-401-9
%F kumari-etal-2026-polyticstamil
%X 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
%U https://aclanthology.org/2026.dravidianlangtech-1.50/
%P 326-330
Markdown (Informal)
[PolyTicsTamil_Alchemists@DravidianLangTech@ACL 2026: An Augmentation-Driven Focal Ensemble Model for Political Sentiment Analysis in Tamil](https://aclanthology.org/2026.dravidianlangtech-1.50/) (Kumari et al., DravidianLangTech 2026)
ACL