How to Build an Amazon Review Dataset

A useful Amazon review dataset begins with a research question, not a desire to collect the largest possible file. Define the product scope, marketplace, fields, time basis, and known coverage limits before downloading rows. Use the Amazon product review export guide to produce the raw file once the design is clear.

Start with a research question

Examples include: Which reliability complaints appear across competing travel kettles? Which benefit phrases occur in highly rated ergonomic mouse reviews? Did AMZShark discover more packaging complaints for a product set during a defined period? Each question implies different filters and validation checks.

Define the product universe

Record direct competitors, substitutes, category leaders, and intentionally excluded products. Store the marketplace with every ASIN. Use a Product Search for a fixed set or a Keyword Search when buyer language defines the scope.

Choose fields based on the analysis

AMZShark's current exports include ASIN, marketplace country code, product title, review URL, rating, discovery date, reviewer URL, reviewer name, review title, and review text. Verified-purchase, Vine, helpful-vote, media, and exact posted-date fields are not currently part of the export, so do not write a research plan that depends on them.

Document the time basis

The date in an AMZShark export is the date the platform discovered the review, not necessarily the date the customer posted it. Use it for dataset snapshots and discovery windows, not for claims about the precise timing of customer sentiment.

Pick a durable format

• CSV for universal interchange and spreadsheet review.
• Excel for a directly shareable workbook.
• JSON for applications expecting an array.
• JSONL for streaming and one-record-per-line processing.
• Parquet for efficient analytical storage.

Create a dataset manifest

Store a small manifest beside every export with the search name, ASIN source, marketplace, filters, discovery-date bounds, format, export time, row count, AMZShark coverage statement, and the person or process responsible for the export. A dataset without provenance is difficult to reproduce or challenge.

Validate before analysis

1. Check required columns and data types.
2. Confirm ratings are between one and five.
3. Confirm every ASIN has a marketplace.
4. Sample source URLs and product assignments.
5. Measure missing titles, text, and reviewer fields.
6. Check unexpected duplicate rows without assuming similar text is identical.
7. Compare row counts before and after each filter.

Separate raw, cleaned, and labeled data

Never overwrite the source export. Keep a raw layer, a cleaned layer with documented transformations, and an analytical layer containing theme or sentiment labels. Version the taxonomy and any prompts or code used to generate labels.

Use the dataset responsibly

Customer reviews are authored content. Preserve provenance, avoid unnecessary personal fields, and assess Amazon's policies, applicable law, licenses, and research-ethics requirements for your intended use. A product-research export is not automatically licensed for public redistribution or model training.