Advancing Sentiment Analysis in Tamil-English Code-Mixed Texts: Challenges and Transformer-Based Solutions
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866910899082625024 |
|---|---|
| author | Krasitskii, Mikhail Kolesnikova, Olga Hernandez, Liliana Chanona Sidorov, Grigori Gelbukh, Alexander |
| author_facet | Krasitskii, Mikhail Kolesnikova, Olga Hernandez, Liliana Chanona Sidorov, Grigori Gelbukh, Alexander |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_23295 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Advancing Sentiment Analysis in Tamil-English Code-Mixed Texts: Challenges and Transformer-Based Solutions Krasitskii, Mikhail Kolesnikova, Olga Hernandez, Liliana Chanona Sidorov, Grigori Gelbukh, Alexander Computation and Language 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. |
| title | Advancing Sentiment Analysis in Tamil-English Code-Mixed Texts: Challenges and Transformer-Based Solutions |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2503.23295 |