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.
Abstract (translated)
在印度，品牌监测的有效性越来越受到“HINGLISH”（印地语和英语混合语言）的挑战。这种混合语言广泛用于如Twitter等平台上的用户生成内容中。传统的自然语言处理（NLP）模型基于单语种数据构建，常常无法解读这种代码混用语言在语法和语义层面的复杂性，导致情感分析不准确及市场洞察误导。 为了解决这一问题，我们提出了一种专门针对Hinglish推文的情感分类框架。我们的方法是通过微调mBERT（多语言BERT），利用其多语言能力来更好地理解印度社交媒体的语言多样性。我们的方法的一个关键组成部分是使用子词分词技术，使模型能够有效处理罗马化Hinglish中常见的拼写变化、俚语和词汇外术语。 这项研究提供了一种现成的AI解决方案，用于品牌情感追踪，并在低资源、代码混用环境中建立了强大的多语言NLP基准。
URL
https://arxiv.org/abs/2601.05091
