Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text

Bharathi Raja Chakravarthi; John P. McCrae; Ruba Priyadharshini; Vigneshwaran Muralidaran

Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text

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arxiv 2006.00206 v1 pith:6RA4HEAM submitted 2020-05-30 cs.CL

Bharathi Raja Chakravarthi , Vigneshwaran Muralidaran , Ruba Priyadharshini , John P. McCrae This is my paper · ORCID

classification cs.CL

keywords corpussentimentanalysiscode-mixedcommentcommentslanguagemedia

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Understanding the sentiment of a comment from a video or an image is an essential task in many applications. Sentiment analysis of a text can be useful for various decision-making processes. One such application is to analyse the popular sentiments of videos on social media based on viewer comments. However, comments from social media do not follow strict rules of grammar, and they contain mixing of more than one language, often written in non-native scripts. Non-availability of annotated code-mixed data for a low-resourced language like Tamil also adds difficulty to this problem. To overcome this, we created a gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. In this paper, we describe the process of creating the corpus and assigning polarities. We present inter-annotator agreement and show the results of sentiment analysis trained on this corpus as a benchmark.

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Comparative Study of Pre-Trained BERT and Large Language Models for Code-Mixed Named Entity Recognition
cs.CL 2025-09 conditional novelty 3.0 of 10

Fine-tuned code-mixed models (HingBERT, HingMBERT, HingRoBERTa) reach higher F1 than multilingual BERTs and zero-shot Google Gemini on Hinglish named entity recognition.

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