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Amazon Multi-Category Review Analysis Data
Product Reviews & Feedback
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"No reviews yet"
Free
About
Gain valuable perspective on consumer behaviour and product reception with this expansive collection of Amazon customer reviews. Spanning a six-year period from 2013 to 2019, the data captures detailed feedback across a diverse range of categories, including smartphones, laptops, books, and home appliances. Beyond standard user ratings and written review texts, this resource features a specialised sentiment analysis layer, categorising each entry into positive or negative sentiment ratings. This enriched structure allows for deeper investigation into customer thoughts and feelings, making it a robust foundation for identifying market trends and assessing product performance over time.
Columns
- Unique_ID: The unique identifier assigned to each customer.
- Category: The specific product category to which the review belongs (e.g., mobile, mobile accessories).
- Review_Header: The short title or summary header of the customer review.
- Review_text: The comprehensive detailed text of the customer's feedback and experience.
- Rating: The original integer rating given by the customer (typically on a 1–5 scale).
- Own_Rating: The derived sentiment analysis rating, classifying the review content (e.g., Positive, Negative).
Distribution
- Format: CSV (Amazon Review Data Web Scrapping.csv)
- Size: Approximately 60,900 valid records (11.75 MB).
- Structure: 6 columns per record.
Usage
- Sentiment Analysis Training: Train and validate Natural Language Processing (NLP) models to detect consumer sentiment.
- Market Trend Analysis: Identify shifting consumer preferences and product popularity across the 2013–2019 timeline.
- Recommender Systems: Enhance recommendation algorithms by correlating detailed review text with numerical ratings.
- Product Development: Extract common complaints or praise points to guide product improvements in categories like electronics.
Coverage
- Time Range: 2013 to 2019.
- Product Scope: Covers various categories with a significant concentration in 'mobile' (37%) and 'mobile accessories' (24%).
- Sentiment Scope: Predominantly 'Positive' sentiment (78%), with 'Negative' sentiment accounting for approximately 15%.
License
CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
Who Can Use It
- Data Scientists: For training and testing NLP and sentiment analysis models.
- Market Researchers: To analyse historic consumer satisfaction trends on Amazon.
- E-commerce Analysts: To benchmark product categories and review distributions.
- Students and Academics: As a clean, accessible resource for statistical analysis and machine learning projects.
Dataset Name Suggestions
- Amazon Consumer Sentiment & Reviews (2013-2019)
- E-commerce Product Feedback & Sentiment Corpus
- Amazon Multi-Category Review Analysis Data
- 6-Year Amazon Customer Ratings and Sentiment
Attributes
Original Data Source: Amazon Multi-Category Review Analysis Data
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