Work

PhucNguyen@DravidianLangTech 2026: Political Multiclass Sentiment Analysis with XLM-RoBERTa and Low-Rank Adaptation

Year: 2026

Type: conference-paper

Abstract: 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 Dravidi-anLangTech@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.

Source: Workshop on Speech, Vision, and Language Technologies for Dravidian Languages (DravidianLangTech)

DOI: 10.18653/v1/2026.dravidianlangtech-1.49

Authors: Dinh Khac Phuc Nguyen, Thìn Đặng Văn

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W7166847725

FWCI:

Cites:

Cited by:

Related to: 10