# Preparing Bengali-English Code-Mixed Corpus for Sentiment Analysis of Indian Languages

arXiv: https://arxiv.org/abs/1803.04000 · HTML: https://mlsanity.com/paper/1803.04000/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2018.

## Built on by (most cited first, 7 of 7)

- [BnSentMix: A Diverse Bengali-English Code-Mixed Dataset for Sentiment Analysis](https://mlsanity.com/paper/2408.08964/) (2024)
- [Bangla Natural Language Processing: A Comprehensive Analysis of Classical, Machine Learning, and Deep Learning Based Methods](https://mlsanity.com/paper/2105.14875/) (2021)
- [Sentiment Analysis of Persian-English Code-mixed Texts](https://mlsanity.com/paper/2102.12700/) (2021)
- [AdvCodeMix: Adversarial Attack on Code-Mixed Data](https://mlsanity.com/paper/2111.00350/) (2021)
- [CoLI-Machine Learning Approaches for Code-mixed Language Identification at the Word Level in Kannada-English Texts](https://mlsanity.com/paper/2211.09847/) (2022)
- [Is Attention always needed? A Case Study on Language Identification from Speech](https://mlsanity.com/paper/2110.03427/) (2021)
- [MixSarc: A Bangla-English Code-Mixed Corpus for Implicit Meaning Identification](https://mlsanity.com/paper/2602.21608/) (2026)

## Abstract (arXiv)

Analysis of informative contents and sentiments of social users has been attempted quite intensively in the recent past. Most of the systems are usable only for monolingual data and fails or gives poor results when used on data with code-mixing property. To gather attention and encourage researchers to work on this crisis, we prepared gold standard Bengali-English code-mixed data with language and polarity tag for sentiment analysis purposes. In this paper, we discuss the systems we prepared to collect and filter raw Twitter data. In order to reduce manual work while annotation, hybrid systems combining rule based and supervised models were developed for both language and sentiment tagging. The final corpus was annotated by a group of annotators following a few guidelines. The gold standard corpus thus obtained has impressive inter-annotator agreement obtained in terms of Kappa values. Various metrics like Code-Mixed Index (CMI), Code-Mixed Factor (CF) along with various aspects (language and emotion) also qualitatively polled the code-mixed and sentiment properties of the corpus.