Amazon Reviews Datasets

The Amazon reviews dataset includes all major data points: URL, product name, product rating,  product rating object, ASIN, product rating count, product reviews number, review header, review text, and a lot more.

Yes, you can get updates to your Amazon reviews dataset on a daily, weekly, monthly, or custom basis.

Yes, you can purchase a Amazon reviews subset that will include only the data points you need. By purchasing a subset, cost is reduced substantially.

You can choose one of the following formats: JSON, ndJSON, CSV, or XLSX.

If you don’t want to purchase a dataset, you can start scraping Amazon reviews data using our Amazon reviews scraper.

Yes, you can request sample data to evaluate the quality and relevance of the information provided. This is a great way to ensure it meets your needs before committing to a full dataset.

Yes, you can request specific data points from the Amazon reviews dataset tailored to your unique needs, ensuring you receive precisely the information you require for your projects.

Absolutely, the Amazon reviews dataset offers seamless API integration, allowing you to effortlessly integrate the data into your CRM, analytics tools, or any other systems you use, streamlining your operations.

The Amazon Reviews Dataset is a comprehensive compilation, capturing a detailed panorama of user experiences on the Amazon platform. With key columns such as product_name, product_rating, and product_reviews_number, it provides a granular view of each product's reception. The dataset incorporates rich qualitative insights, including review_text, review_header, and author_name, allowing for a nuanced exploration of consumer sentiments. Furthermore, essential details like brand, asin, and review_posted_date contribute to a deeper understanding of product-specific dynamics. The inclusion of author-specific attributes such as author_id and author_link enhances the dataset's richness, fostering analyses that span both individual reviews and broader trends. This versatile resource, enriched with quantitative metrics like helpful_count and review_images, becomes an invaluable asset for researchers and analysts seeking to decipher the multifaceted landscape of consumer feedback within the vast expanse of Amazon's e-commerce ecosystem.