推进泰米尔语-英语混合文本中的情感分析：挑战与基于Transformer的解决方案
Advancing Sentiment Analysis in Tamil-English Code-Mixed Texts: Challenges and Transformer-Based Solutions
AI总结：
本文针对泰米尔语-英语混合文本的情感分析挑战，评估了XLM-RoBERTa等Transformer模型在低资源环境中的表现，并指出数据增强与混合建模是未来提升准确率的关键方向。
摘要：
使用先进的基于Transformer的模型探索了泰米尔语-英语混合文本中的情感分析任务。解决了语法不一致、拼写变异和语音歧义带来的挑战。考察了现有数据集的局限性和标注空白，强调需要更大、更多样的语料库。在低资源、混合环境中评估了包括XLM-RoBERTa、mT5、IndicBERT和RemBERT在内的Transformer架构。分析了性能指标，突出了特定模型在处理多语言情感分类中的有效性。研究结果表明，需要进一步推进数据增强、语音规范化和混合建模方法以提高准确性。提出了改进混合文本情感分析的未来研究方向。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
