Bangla and Banglish E-Commerce Reviews Dataset for Aspect-Based Sentiment Analysis
Description
This dataset is a manually annotated collection of 3,587 Bangla and Banglish e-commerce product reviews, curated for Aspect-Based Sentiment Analysis (ABSA) research in low-resource languages. Reviews were collected from Daraz, covering products in the electronics domain such as mobile phones, laptops, earphones, headphones, and CCTV cameras. The raw dataset initially contained 19,638 reviews, which were preprocessed to remove noise, normalize text, and filter irrelevant entries. Preprocessing steps included removing URLs, emails, and mentions; converting emojis to text; normalizing Banglish; reducing character elongation; ensuring meaningful sentence boundaries; and removing all reviews shorter than 5 words to maintain data quality and reliability. After cleaning, 10,657 reviews were retained. A subset of 3,587 reviews was manually annotated for ABSA. Each review contains one or more aspect-level sentiment labels (positive, negative, or neutral) covering five aspects: product quality, price, delivery, packaging, and seller service. Dataset Counts: Total original reviews: 19,638 Total preprocessed reviews: 10,657 Total annotated reviews: 3,587 Languages (preprocessed): Bangla: 6,731 Banglish: 2,797 Mixed: 1,129 Folder Structure: original/ – raw scraped reviews preprocessed/ – cleaned and normalized reviews, filtered for quality annotated/ – manually labeled ABSA reviews metadata/ – summary of review counts, language distribution Data quality was ensured through duplicate removal, filtering short reviews, manual validation, and annotation consistency checks. This dataset is ready to train and evaluate multilingual transformer models such as BanglaBERT, XLM-RoBERTa, and mBERT for multi-label ABSA tasks, and serves as a benchmark for Bangla–Banglish e-commerce sentiment research.