# An Ensemble Model for Sentiment Analysis of Hindi-English Code-Mixed Data

arXiv: https://arxiv.org/abs/1806.04450 · HTML: https://mlsanity.com/paper/1806.04450/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2018.

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

- [Sentiment Analysis of Persian-English Code-mixed Texts](https://mlsanity.com/paper/2102.12700/) (2021)
- [On the logistical difficulties and findings of Jopara Sentiment Analysis](https://mlsanity.com/paper/2105.02947/) (2021)
- [CONFLATOR: Incorporating Switching Point based Rotatory Positional Encodings for Code-Mixed Language Modeling](https://mlsanity.com/paper/2309.05270/) (2023)
- [Sentiment Analysis and Sarcasm Detection of Indian General Election Tweets](https://mlsanity.com/paper/2201.02127/) (2022)

## Abstract (arXiv)

In multilingual societies like India, code-mixed social media texts comprise the majority of the Internet. Detecting the sentiment of the code-mixed user opinions plays a crucial role in understanding social, economic and political trends. In this paper, we propose an ensemble of character-trigrams based LSTM model and word-ngrams based Multinomial Naive Bayes (MNB) model to identify the sentiments of Hindi-English (Hi-En) code-mixed data. The ensemble model combines the strengths of rich sequential patterns from the LSTM model and polarity of keywords from the probabilistic ngram model to identify sentiments in sparse and inconsistent code-mixed data. Experiments on reallife user code-mixed data reveals that our approach yields state-of-the-art results as compared to several baselines and other deep learning based proposed methods.