Abstract
The sentiment analysis task in Tamil-English code-mixed texts has been explored using advanced transformer-based models. Challenges from grammatical inconsistencies, orthographic variations, and phonetic ambiguities have been addressed. The limitations of existing datasets and annotation gaps have been examined, emphasizing the need for larger and more diverse corpora. Transformer architectures, including XLM-RoBERTa, mT5, IndicBERT, and RemBERT, have been evaluated in low-resource, code-mixed environments. Performance metrics have been analyzed, highlighting the effectiveness of specific models in handling multilingual sentiment classification. The findings suggest that further advancements in data augmentation, phonetic normalization, and hybrid modeling approaches are required to enhance accuracy. Future research directions for improving sentiment analysis in code-mixed texts have been proposed.
Abstract (translated)
在泰米尔语和英语混合文本中的情感分析任务已经通过高级的基于Transformer模型进行了探索。针对语法不一致、拼写变化以及语音模糊等挑战，研究已提出解决方案。现有数据集的局限性和标注缺口也被详细审查，强调了需要更大且更具多样性的语料库。在低资源、代码混编环境中，包括XLM-RoBERTa、mT5、IndicBERT和RemBERT在内的Transformer架构已被评估。性能度量被分析，并突出了特定模型在处理多语言情感分类时的有效性。研究结果表明，为了提高准确性，需要进一步发展数据增强、语音规范化以及混合建模方法。提出了未来改进代码混编文本中情感分析的研究方向。
URL
https://arxiv.org/abs/2503.23295
