# HinglishNLP: Fine-tuned Language Models for Hinglish Sentiment Detection

arXiv: https://arxiv.org/abs/2008.09820 · HTML: https://mlsanity.com/paper/2008.09820/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2020.

## Built on by (most cited first, 5 of 5)

- [Memotion 3: Dataset on Sentiment and Emotion Analysis of Codemixed Hindi-English Memes](https://mlsanity.com/paper/2303.09892/) (2023)
- [Overview of Memotion 3: Sentiment and Emotion Analysis of Codemixed Hinglish Memes](https://mlsanity.com/paper/2309.06517/) (2023)
- [Multimodal Sentiment Analysis Based on Causal Reasoning](https://mlsanity.com/paper/2412.07292/) (2024)
- [Multilingual Hate Speech and Offensive Content Detection using Modified Cross-entropy Loss](https://mlsanity.com/paper/2202.02635/) (2022)
- [Training-Time Explainability for Multilingual Hate Speech Detection: Aligning Model Reasoning with Human Rationales](https://mlsanity.com/paper/2608.26125/) (2026)

## Abstract (arXiv)

Sentiment analysis for code-mixed social media text continues to be an under-explored area. This work adds two common approaches: fine-tuning large transformer models and sample efficient methods like ULMFiT. Prior work demonstrates the efficacy of classical ML methods for polarity detection. Fine-tuned general-purpose language representation models, such as those of the BERT family are benchmarked along with classical machine learning and ensemble methods. We show that NB-SVM beats RoBERTa by 6.2% (relative) F1. The best performing model is a majority-vote ensemble which achieves an F1 of 0.707. The leaderboard submission was made under the codalab username nirantk, with F1 of 0.689.