--- 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 --- ๐Ÿ“Š 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: ๐Ÿ†” Id ๐Ÿ›’ ProductId ๐Ÿ’ฌ Code-Mixed-Text ๐Ÿ’ก Sentiment The dataset captures diverse linguistic styles, authentic code-mixing, and real-world sentiment patterns from multilingual digital communication. ๐ŸŒ Text Distribution The dataset includes four major text categories, representing different levels of Englishโ€“Bengali mixing. Each group is evenly structured across the dataset for balanced linguistic coverage. ๐Ÿ‡ฌ๐Ÿ‡ง English Texts: 15,000 โ–“โ–“โ–“โ–“โ–“โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ 15% ๐Ÿ‡ง๐Ÿ‡ฉ Bengali Texts: 15,000 โ–“โ–“โ–“โ–“โ–“โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ 15% ๐ŸŒ Englishโ€“Bengali Mixed Texts: 35,000 โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ 35% ๐Ÿ”ค Englishโ€“Roman Bengali Mixed: 35,000 โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ 35% ๐Ÿงฎ Total Samples: 100,000 ๐Ÿ“˜ This distribution ensures the dataset provides a rich combination of monolingual and code-mixed samples suitable for multilingual model training. ๐Ÿงฉ 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. โœด๏ธ English Words: 6,870,500 โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“ 71.5% ๐Ÿ”ก Bengali Words: 2,136,460 โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ 22.2% ๐Ÿ”  Roman Bengali Words: 601,220 โ–“โ–“โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ 6.3% ๐Ÿงพ Total Word Count: 9,608,180 ๐Ÿ“™ The mix of three distinct scripts provides valuable linguistic variability, allowing models to learn fine-grained lexical and orthographic distinctions. ๐Ÿ’ฌ 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 โ€” where users share more positive experiences. ๐Ÿ˜Š Positive Reviews: 79.3% โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–“โ–‘โ–‘ 79,300 ๐Ÿ˜  Negative Reviews: 20.7% โ–“โ–“โ–“โ–“โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘โ–‘ 20,700 ๐Ÿ’ก Total Samples: 100,000 ๐Ÿ“— This realistic sentiment imbalance provides a natural testing ground for building sentiment classification models robust to class skew. ๐Ÿ“ˆ 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: ๐ŸŒ Four-level text distribution โ€” English, Bengali, Englishโ€“Bengali, Englishโ€“Roman Bengali ๐Ÿงฉ 9.6M words across three languages ๐Ÿ’ฌ Natural sentiment imbalance (79.3% positive, 20.7% negative) ๐Ÿ—ฃ๏ธ Rich linguistic variation for bilingual and transliterated text โš™๏ธ Ideal for: Code-Mixed Sentiment Analysis Language Identification Cross-Lingual Embedding Learning Multilingual Model Evaluation