The widespread availability of code-mixed data can provide valuable insights into low-resource languages like Bengali, which have limited datasets. Sentiment analysis has been a fundamental text classification task across several languages for code-mixed data. However, there has yet to be a large-scale and diverse sentiment analysis dataset on code-mixed Bengali. We address this limitation by introducing BnSentMix, a sentiment analysis dataset on code-mixed Bengali consisting of 20,000 samples with 4 sentiment labels from Facebook, YouTube, and e-commerce sites. We ensure diversity in data sources to replicate realistic code-mixed scenarios. Additionally, we propose 14 baseline methods including novel transformer encoders further pre-trained on code-mixed Bengali-English, achieving an overall accuracy of 69.8% and an F1 score of 69.1% on sentiment classification tasks. Detailed analyses reveal variations in performance across different sentiment labels and text types, highlighting areas for future improvement.
@article{arxiv.2408.08964,
title = {BnSentMix: A Diverse Bengali-English Code-Mixed Dataset for Sentiment Analysis},
author = {Sadia Alam and Md Farhan Ishmam and Navid Hasin Alvee and Md Shahnewaz Siddique and Md Azam Hossain and Abu Raihan Mostofa Kamal},
journal= {arXiv preprint arXiv:2408.08964},
year = {2024}
}