EnTaCs: Analyzing the Relationship Between Sentiment and Language Choice in English-Tamil Code-Switching
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866912985259180032 |
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| author | Bontempo, Paul |
| author_facet | Bontempo, Paul |
| contents | This paper investigates the relationship between utterance sentiment and language choice in English-Tamil code-switched text, using methods from machine learning and statistical modelling. We apply a fine-tuned XLM-RoBERTa model for token-level language identification on 35,650 romanized YouTube comments from the DravidianCodeMix dataset, producing per-utterance measurements of English proportion and language switch frequency. Linear regression analysis reveals that positive utterances exhibit significantly greater English proportion (34.3%) than negative utterances (24.8%), and mixed-sentiment utterances show the highest language switch frequency when controlling for utterance length. These findings support the hypothesis that emotional content demonstrably influences language choice in multilingual code-switching settings, due to socio-linguistic associations of prestige and identity with embedded and matrix languages. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_26587 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | EnTaCs: Analyzing the Relationship Between Sentiment and Language Choice in English-Tamil Code-Switching Bontempo, Paul Computation and Language This paper investigates the relationship between utterance sentiment and language choice in English-Tamil code-switched text, using methods from machine learning and statistical modelling. We apply a fine-tuned XLM-RoBERTa model for token-level language identification on 35,650 romanized YouTube comments from the DravidianCodeMix dataset, producing per-utterance measurements of English proportion and language switch frequency. Linear regression analysis reveals that positive utterances exhibit significantly greater English proportion (34.3%) than negative utterances (24.8%), and mixed-sentiment utterances show the highest language switch frequency when controlling for utterance length. These findings support the hypothesis that emotional content demonstrably influences language choice in multilingual code-switching settings, due to socio-linguistic associations of prestige and identity with embedded and matrix languages. |
| title | EnTaCs: Analyzing the Relationship Between Sentiment and Language Choice in English-Tamil Code-Switching |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2603.26587 |