Hierarchical Amazon Reviews for Classification

Columns

• productId: A unique identifier for the product.
• Title: The headline or title given to the product review.
• userId: A unique identifier for the user who submitted the review.
• Helpfulness: Indicates whether the review was found helpful by other users.
• Score: The rating assigned by other users to the review, typically on a scale of 1 to 5.
• Time: The timestamp of when the review was submitted.
• Text: The main body or content of the product review.
• Cat1: The primary, level 1 class name or category assigned to the product.
• Cat2: The secondary, level 2 class name or sub-category.
• Cat3: The most specific, level 3 class name or sub-sub-category.

Distribution

• Product IDs: Over 20,851 unique product identifiers are present.
• User IDs: There are over 19,598 unique user identifiers.
• Helpfulness: Around 38% of reviews have a 0/0 helpfulness score, while 15% are rated 1/1.
• Score: Review scores range from 1 to 5, with a notable portion (23,362 records) falling into the 4.80-5.00 range.
• Time: Timestamps span a wide period, with a large concentration of reviews (16,331 records) between roughly 1277000639.95 and 1344211200.00.
• Level 1 Categories (Cat1): There are 6 distinct top-level categories, including "toys games" (26% of reviews) and "health personal care" (24% of reviews). The full list of level 1 classes includes: health personal care, toys games, beauty, pet supplies, baby products, and grocery gourmet food.
• Level 2 Categories (Cat2): There are 64 sub-categories, with examples like "personal care" (7%) and "dogs" (7%).
• Level 3 Categories (Cat3): The most granular level contains 510 unique categories, with "shaving hair removal" being an example.

Usage

• Developing and testing various hierarchical text classification methodologies.
• Training multi-class models, whether through a flat approach (concatenating class names) or a simple hierarchical approach (sequential classification).
• Language model fine-tuning, particularly with the raw, unlabelled review data provided.
• Building systems for sentiment analysis and product categorisation.
• Market research and identifying product trends based on consumer reviews.

Coverage

License

Who Can Use It

• Data Scientists and Machine Learning Engineers focusing on natural language processing (NLP) and classification tasks.
• Academics and Researchers studying hierarchical classification techniques and consumer behaviour.
• Developers looking to fine-tune language models with real-world product review data.
• Businesses aiming to improve product categorisation, sentiment analysis, or customer insight generation.

Dataset Name Suggestions

• Amazon Product Review Hierarchy
• Hierarchical Amazon Reviews for Classification
• Product Review Categorisation Dataset
• Amazon Consumer Review Data

Attributes