BnSentMix: A Diverse Bengali-English Code-Mixed Dataset for Sentiment Analysis
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909421667352576 |
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| author | Alam, Sadia Ishmam, Md Farhan Alvee, Navid Hasin Siddique, Md Shahnewaz Hossain, Md Azam Kamal, Abu Raihan Mostofa |
| author_facet | Alam, Sadia Ishmam, Md Farhan Alvee, Navid Hasin Siddique, Md Shahnewaz Hossain, Md Azam Kamal, Abu Raihan Mostofa |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_08964 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BnSentMix: A Diverse Bengali-English Code-Mixed Dataset for Sentiment Analysis Alam, Sadia Ishmam, Md Farhan Alvee, Navid Hasin Siddique, Md Shahnewaz Hossain, Md Azam Kamal, Abu Raihan Mostofa Computation and Language 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. |
| title | BnSentMix: A Diverse Bengali-English Code-Mixed Dataset for Sentiment Analysis |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2408.08964 |