# JU_KS@SAIL_CodeMixed-2017: Sentiment Analysis for Indian Code Mixed Social Media Texts

arXiv: https://arxiv.org/abs/1802.05737 · HTML: https://mlsanity.com/paper/1802.05737/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2018.

## Built on by (most cited first, 1 of 1)

- [ExCode-Mixed: Explainable Approaches towards Sentiment Analysis on Code-Mixed Data using BERT models](https://mlsanity.com/paper/2109.03200/) (2021)

## Abstract (arXiv)

This paper reports about our work in the NLP Tool Contest @ICON-2017, shared task on Sentiment Analysis for Indian Languages (SAIL) (code mixed). To implement our system, we have used a machine learning algo-rithm called Multinomial Na"ive Bayes trained using n-gram and SentiWordnet features. We have also used a small SentiWordnet for English and a small SentiWordnet for Bengali. But we have not used any SentiWordnet for Hindi language. We have tested our system on Hindi-English and Bengali-English code mixed social media data sets released for the contest. The performance of our system is very close to the best system participated in the contest. For both Bengali-English and Hindi-English runs, our system was ranked at the 3rd position out of all submitted runs and awarded the 3rd prize in the contest.