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Amazon Product Review Data
Product Reviews & Feedback
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Free
About
568,000 consumer reviews for various products found on amazon.com. It offers a rich resource for understanding customer sentiment and product feedback, making it highly valuable for developing and testing machine learning models, particularly in areas like deep learning and multiclass classification within e-commerce services. The data provides significant insights into customer opinions and product interactions.
Columns
The dataset is structured with 10 key attributes, each offering a distinct piece of information:
- Id: A unique identifier for each record.
- ProductId: Identifies the specific product being reviewed.
- UserId: Identifies the customer who submitted the review.
- ProfileName: The public profile name of the user who provided the review.
- HelpfulnessNumerator: The number of users who found the review helpful.
- HelpfulnessDenominator: The total number of users who rated the review's helpfulness.
- Score: The product rating given by the user, on a scale typically from 1 to 5.
- Time: The timestamp indicating when the review was submitted.
- Summary: A concise summary provided by the reviewer.
- Text: The actual, full text of the customer review.
Distribution
The dataset is provided as a CSV file named
Reviews.csv, with a file size of approximately 300.9 MB. It contains a total of 568,454 records across its 10 columns. All records across all columns are validated as 100% complete and free from missing or mismatched entries, ensuring data integrity. The dataset includes 74,258 unique Product IDs and 256,059 unique User IDs. Product ratings range from 1 to 5, with a mean score of 4.18. Review timestamps span a broad period, from approximately 939 million to 1.35 billion (Unix epoch time), indicating a wide collection period.Usage
This dataset is an excellent foundation for various applications, including:
- Sentiment Analysis: Training models to determine the emotional tone of customer reviews.
- Product Recommendation Systems: Utilising review patterns to suggest relevant products to users.
- Natural Language Processing (NLP): Developing and evaluating algorithms for text understanding and generation.
- E-commerce Trend Analysis: Identifying popular products, common complaints, and emerging customer preferences.
- Machine Learning and Deep Learning Research: Providing a large-scale, real-world text dataset for model development and benchmarking.
Coverage
The data originates from amazon.com, reflecting consumer feedback on a multitude of products available on the platform. Review timestamps cover a substantial period, providing a historical perspective on product reception. While specific geographic or demographic details of reviewers are not provided, the dataset represents a wide array of Amazon customers.
License
CC0: Public Domain
Who Can Use It
This dataset is ideal for:
- Data Scientists and Machine Learning Engineers: For training and evaluating NLP models, sentiment analysis, and recommendation algorithms.
- E-commerce Businesses and Analysts: To gain insights into customer satisfaction, identify areas for product improvement, and monitor brand perception.
- Academic Researchers: For studies on consumer behaviour, digital marketing, and large-scale text analysis.
Dataset Name Suggestions
- Amazon Product Review Data
- E-commerce Customer Feedback
- Online Product Reviews
- Amazon Sentiment Dataset
- Consumer Product Opinions
Attributes
Original Data Source: Amazon Product Review Data
QUALITY