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arXiv · 2408.08964

BnSentMix: A Diverse Bengali-English Code-Mixed Dataset for Sentiment Analysis

Sadia Alam·Md Farhan Ishmam·Navid Hasin Alvee·Md Shahnewaz Siddique·Md Azam Hossain·Abu Raihan Mostofa Kamal
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Abstract

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.

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Sadia Alam, Md Farhan Ishmam, Navid Hasin Alvee, Md Shahnewaz Siddique, Md Azam Hossain, Abu Raihan Mostofa Kamal. 2024-08-16. BnSentMix: A Diverse Bengali-English Code-Mixed Dataset for Sentiment Analysis. https://arxiv.org/abs/2408.08964

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