Hinglish Sentiment, Upgraded: Smarter Brand Listening for India
Making Sense of Hinglish on Twitter
From “yaar this movie was lit” to “service bilkul bakwaas,” Indian Twitter blends Hindi + English—Hinglish. Traditional NLP tools, built for a single language, often misread this mix, leading brands astray.
This research builds a high-performance sentiment classifier for Hinglish tweets. It fine-tunes mBERT (a multilingual AI model) and uses subword tokenization so the system recognizes spelling variations, slang, and Romanized Hindi–English words.
- Why it matters: More reliable brand monitoring and market insights in India’s real online language.
- How it works: Multilingual pretraining + subword pieces handle code-mixing, typos, and out‑of‑vocabulary terms.
- What it means: A production-ready approach and a strong benchmark for NLP in low-resource, code-mixed settings.
Read the paper: https://arxiv.org/abs/2601.05091v1
Paper: https://arxiv.org/abs/2601.05091v1
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