This is a dataset for the sentiment analysis task. It contains 100k code mixed data. The languages are Bangla-English-Hindi.
Initially, we select the Amazon Review Dataset as our base data, referenced from Ni et al. (2019)1. We randomly extract 100,000 instances from this dataset. The original labels in this dataset are ratings, scaled from 1 to 5. For our specific task, we categorize them into Positive (rating > 3), Neutral (rating = 3), and Negative (rating < 3), ensuring a balanced number of instances for each label. To generate the synthetic Code-mixed dataset, we apply two distinct methodologies: the Random Code-mixing Algorithm by Krishnan et al. (2021)2 and r-CM by Santy et al. (2021)3.
Label | Count | Percentage |
---|---|---|
Negative | 20000 | 33.33% |
Neutral | 20000 | 33.33% |
Positive | 19999 | 33.33% |
Label | Count | Percentage |
---|---|---|
Neutral | 6667 | 33.34% |
Positive | 6667 | 33.34% |
Negative | 6666 | 33.33% |
Label | Count | Percentage |
---|---|---|
Negative | 6667 | 33.34% |
Positive | 6667 | 33.34% |
Neutral | 6666 | 33.33% |
If you utilize this dataset, kindly cite our paper.
@article{raihan2023mixed,
title={Mixed-Distil-BERT: Code-mixed Language Modeling for Bangla, English, and Hindi},
author={Raihan, Md Nishat and Goswami, Dhiman and Mahmud, Antara},
journal={arXiv preprint arXiv:2309.10272},
year={2023}
}
Footnotes
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Ni, J., Li, J., & McAuley, J. (2019). Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP) (pp. 188-197). ↩
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Krishnan, J., Anastasopoulos, A., Purohit, H., & Rangwala, H. (2021). Multilingual code-switching for zero-shot cross-lingual intent prediction and slot filling. arXiv preprint arXiv:2103.07792. ↩
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Santy, S., Srinivasan, A., & Choudhury, M. (2021). BERTologiCoMix: How does code-mixing interact with multilingual BERT? In Proceedings of the Second Workshop on Domain Adaptation for NLP (pp. 111-121). Santy, S., Srinivasan, A., & Choudhury, M. (2021). BERTologiCoMix: How does code-mixing interact with multilingual BERT? In Proceedings of the Second Workshop on Domain Adaptation for NLP (pp. 111-121). ↩ ↩