# Sentiment Analysis of Code-Mixed Languages leveraging Resource Rich Languages

arXiv: https://arxiv.org/abs/1804.00806 · HTML: https://mlsanity.com/paper/1804.00806/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2018.

## Built on by (most cited first, 4 of 4)

- [Multi-Task Text Classification using Graph Convolutional Networks for Large-Scale Low Resource Language](https://mlsanity.com/paper/2205.01204/) (2022)
- [Reasoning Beyond Labels: Measuring LLM Sentiment in Low-Resource, Culturally Nuanced Contexts](https://mlsanity.com/paper/2508.04199/) (2025)
- [AtteSTNet -- An attention and subword tokenization based approach for code-switched text hate speech detection](https://mlsanity.com/paper/2112.11479/) (2021)
- [CARBD-Ko: A Contextually Annotated Review Benchmark Dataset for Aspect-Level Sentiment Classification in Korean](https://mlsanity.com/paper/2402.15046/) (2024)

## Abstract (arXiv)

Code-mixed data is an important challenge of natural language processing because its characteristics completely vary from the traditional structures of standard languages. In this paper, we propose a novel approach called Sentiment Analysis of Code-Mixed Text (SACMT) to classify sentences into their corresponding sentiment - positive, negative or neutral, using contrastive learning. We utilize the shared parameters of siamese networks to map the sentences of code-mixed and standard languages to a common sentiment space. Also, we introduce a basic clustering based preprocessing method to capture variations of code-mixed transliterated words. Our experiments reveal that SACMT outperforms the state-of-the-art approaches in sentiment analysis for code-mixed text by 7.6% in accuracy and 10.1% in F-score.