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--- |
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license: mit |
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language: |
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- en |
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- bn |
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tags: |
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- En-Bn-Code-Mixed-Two-Class-Sentiment-Dataset |
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- code-mixed-sentiment-analysis |
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pretty_name: En-Bn-Code-Mixed-Two-Class-Sentiment-Dataset |
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--- |
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📊 En-Bn-Code-Mixed-Two-Class-Sentiment-Dataset |
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The En-Bn-Code-Mixed-Two-Class-Sentiment-Dataset is a multilingual dataset of 100,000 product review texts designed for code-mixed sentiment analysis involving English, Bengali, and Roman Bengali. |
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Each record includes: |
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🆔 Id |
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🛒 ProductId |
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💬 Code-Mixed-Text |
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💡 Sentiment |
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The dataset captures diverse linguistic styles, authentic code-mixing, and real-world sentiment patterns from multilingual digital communication. |
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🌐 Text Distribution |
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The dataset includes four major text categories, representing different levels of English–Bengali mixing. |
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Each group is evenly structured across the dataset for balanced linguistic coverage. |
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🇬🇧 English Texts: 15,000 ▓▓▓▓▓░░░░░░░░░░░░░░░░░ 15% |
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🇧🇩 Bengali Texts: 15,000 ▓▓▓▓▓░░░░░░░░░░░░░░░░░ 15% |
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🌍 English–Bengali Mixed Texts: 35,000 ▓▓▓▓▓▓▓▓▓▓▓▓░░░░░░░░░░ 35% |
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🔤 English–Roman Bengali Mixed: 35,000 ▓▓▓▓▓▓▓▓▓▓▓▓░░░░░░░░░░ 35% |
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🧮 Total Samples: 100,000 |
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📘 This distribution ensures the dataset provides a rich combination of monolingual and code-mixed samples suitable for multilingual model training. |
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🧩 Word Distribution |
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The dataset exhibits extensive lexical diversity across three language layers. |
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Word counts demonstrate the dominance of English syntax, with embedded Bengali and Roman Bengali expressions adding cultural and emotional context. |
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✴️ English Words: 6,870,500 ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ 71.5% |
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🔡 Bengali Words: 2,136,460 ▓▓▓▓▓▓▓▓░░░░░░░░░░░░░░░░░░ 22.2% |
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🔠 Roman Bengali Words: 601,220 ▓▓░░░░░░░░░░░░░░░░░░░░░░░░ 6.3% |
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🧾 Total Word Count: 9,608,180 |
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📙 The mix of three distinct scripts provides valuable linguistic variability, allowing models to learn fine-grained lexical and orthographic distinctions. |
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💬 Sentiment Distribution |
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The dataset follows a binary sentiment structure with positive and negative review labels. |
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Unlike a perfectly balanced dataset, it naturally reflects real-world customer behavior — where users share more positive experiences. |
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😊 Positive Reviews: 79.3% ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓░░ 79,300 |
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😠 Negative Reviews: 20.7% ▓▓▓▓░░░░░░░░░░░░░░░░░░░░░░ 20,700 |
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💡 Total Samples: 100,000 |
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📗 This realistic sentiment imbalance provides a natural testing ground for building sentiment classification models robust to class skew. |
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📈 Summary |
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The En-Bn-Code-Mixed-Two-Class-Sentiment-Dataset provides a comprehensive resource for multilingual NLP and code-mixed text analysis. |
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Key highlights include: |
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🌐 Four-level text distribution — English, Bengali, English–Bengali, English–Roman Bengali |
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🧩 9.6M words across three languages |
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💬 Natural sentiment imbalance (79.3% positive, 20.7% negative) |
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🗣️ Rich linguistic variation for bilingual and transliterated text |
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⚙️ Ideal for: |
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Code-Mixed Sentiment Analysis |
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Language Identification |
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Cross-Lingual Embedding Learning |
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Multilingual Model Evaluation |
