Code-Mix Sentiment Analysis on Hinglish Tweets

TL;DR

This paper presents a specialized sentiment analysis framework for Hinglish tweets, utilizing mBERT with subword tokenization to improve accuracy in understanding code-mixed social media content.

Contribution

It introduces a fine-tuned mBERT model with subword tokenization tailored for Hinglish, addressing the challenges of code-mixed language in sentiment analysis.

Findings

Achieved high accuracy in Hinglish sentiment classification

Established a new benchmark for multilingual NLP in low-resource settings

Provided a production-ready AI tool for brand sentiment tracking

Abstract

The effectiveness of brand monitoring in India is increasingly challenged by the rise of Hinglish--a hybrid of Hindi and English--used widely in user-generated content on platforms like Twitter. Traditional Natural Language Processing (NLP) models, built for monolingual data, often fail to interpret the syntactic and semantic complexity of this code-mixed language, resulting in inaccurate sentiment analysis and misleading market insights. To address this gap, we propose a high-performance sentiment classification framework specifically designed for Hinglish tweets. Our approach fine-tunes mBERT (Multilingual BERT), leveraging its multilingual capabilities to better understand the linguistic diversity of Indian social media. A key component of our methodology is the use of subword tokenization, which enables the model to effectively manage spelling variations, slang, and out-of-vocabulary terms common in Romanized Hinglish. This research delivers a production-ready AI solution for brand sentiment tracking and establishes a strong benchmark for multilingual NLP in low-resource, code-mixed environments.