Hinglish Sentiment, Upgraded: Smarter Brand Listening for India

Kari Jaaskelainen

10 Jan 2026 — 1 min read

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

Register: https://www.AiFeta.com

#Hinglish #SentimentAnalysis #NLP #AI #mBERT #SocialMedia #India #BrandMonitoring

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Making Sense of Hinglish on Twitter

Read more

Meet GRACE: a moral governor for safer, more transparent AI

Why some fine-tuned LLMs miss phishing—and how to fix it

AI that explains itself—by following the science

Meet GenomAgent: A Team of AI Specialists for Smarter Genomics Q&A