Advancing Sentiment Analysis in T amil- E nglish Code-Mixed Texts: Challenges and Transformer-Based Solutions (2025)

Abstract: This study examines sentiment analysis in Tamil-English code-mixed texts using advanced transformer-based architectures. The unique linguistic challenges, including mixed grammar, orthographic variability, and phonetic inconsistencies, are addressed. Data limitations and annotation gaps are discussed, highlighting the need for larger datasets. The performance of models such as XLM-RoBERTa, mT5, IndicBERT, and RemBERT is evaluated, with insights into their optimization for low-resource, code-mixed environments.

## introduction sentiment analysis in code-mixed texts has attracted significant attention from researchers in the field of natural language processing (nlp) due to the growing prevalence of bilingual communication in digital environments @xcite . code-mixing refers to combining two or more languages within a single text or utterance, a phenomenon commonly observed in multilingual communities @xcite . one such linguistic combination is tamil-english code-mixing, frequently found in social media, text messages, and informal communication @xcite . despite the relevance of this issue, existing approaches to processing code-mixed texts face several challenges. key linguistic difficulties include the mixing of grammatical structures, orthographic variability, and phonetic inconsistencies @xcite . for example, texts may contain elements of the tamil language in their original script as well as its transliteration into the latin alphabet, complicating tokenization and analysis @xcite . moreover, the lack of annotated data for training machine learning models remains an unresolved issue @xcite . although efforts have been made to create datasets @xcite , they remain limited in size and coverage. this impacts prediction quality even when using advanced transformer-based models such as xlm-roberta, mt5, indicbert, and rembert @xcite @xcite . this study provides a detailed analysis of the influence of linguistic factors on the performance of sentiment analysis models in tamil-english codemixed texts. alternative transformer architectures are examined, along with an evaluation of existing datasets. the conclusion discusses promising directions for further development in this field, including expanding dataset sizes and integrating linguistic rules into model architectures @xcite ).