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