BnSentMix: A Diverse Bengali-English Code-Mixed Dataset for Sentiment Analysis
<|reference_start|>BnSentMix: A Diverse Bengali-English Code-Mixed Dataset for Sentiment Analysis: 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.<|reference_end|>
@article{alam2024bnsentmix:,
    title={BnSentMix: A Diverse Bengali-English Code-Mixed Dataset for Sentiment
  Analysis},
    author={Sadia Alam, Md Farhan Ishmam, Navid Hasin Alvee, Md Shahnewaz
  Siddique, Md Azam Hossain, Abu Raihan Mostofa Kamal},
    journal={arXiv preprint arXiv:2408.08964},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.08964},
    primaryClass={cs.CL}
}