# Unsupervised Sentiment Analysis for Code-mixed Data

arXiv: https://arxiv.org/abs/2001.11384 · HTML: https://mlsanity.com/paper/2001.11384/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jan 2020.

## Built on by (most cited first, 2 of 2)

- [JU_NLP at HinglishEval: Quality Evaluation of the Low-Resource Code-Mixed Hinglish Text](https://mlsanity.com/paper/2206.08053/) (2022)
- [Enhancing Affinity Propagation for Improved Public Sentiment Insights](https://mlsanity.com/paper/2410.12862/) (2024)

## Abstract (arXiv)

Code-mixing is the practice of alternating between two or more languages. Mostly observed in multilingual societies, its occurrence is increasing and therefore its importance. A major part of sentiment analysis research has been monolingual, and most of them perform poorly on code-mixed text. In this work, we introduce methods that use different kinds of multilingual and cross-lingual embeddings to efficiently transfer knowledge from monolingual text to code-mixed text for sentiment analysis of code-mixed text. Our methods can handle code-mixed text through a zero-shot learning. Our methods beat state-of-the-art on English-Spanish code-mixed sentiment analysis by absolute 3% F1-score. We are able to achieve 0.58 F1-score (without parallel corpus) and 0.62 F1-score (with parallel corpus) on the same benchmark in a zero-shot way as compared to 0.68 F1-score in supervised settings. Our code is publicly available.