randarium
Datasets

Fake Review Dataset Generator

Create reproducible synthetic product review datasets with ratings, titles, review bodies, author names, dates, and verification status. Easily control minimum rating to test review filtering and analytics.

Also known as: mock reviews · dummy reviews · customer feedback

seeded · synthetic data

Output

No output yet — set your options and hit .
About this tool, tips & examples

What it does

The Fake Review Dataset Generator produces synthetic product reviews — star ratings, titles, review bodies, author names, dates, and verified-purchase flags — up to 10,000 rows per run. A minimum-rating control skews the dataset positive (or, left at 1, keeps the full spread), and a null rate simulates incomplete records. Seeded, so any dataset can be regenerated exactly.

Common use cases

  • Review system development — lists, rating summaries, and sort/filter logic against varied reproducible reviews.
  • Analytics and moderation tooling — rating distributions, verified-vs-unverified splits, and trend charts.
  • Recommendation engine fixtures — controlled rating data for testing scoring pipelines.
  • Rating widget demos — star breakdowns and histograms with numbers that look lived-in.

Settings

  • Rows — 1 to 10,000 reviews per run.
  • Minimum rating — 1 to 5; raise it to simulate a curated or suspiciously happy dataset, keep it at 1 for realistic spread.
  • Null rate — 0 to 1; randomly blanks nullable fields for messy-import testing.
  • Seed — identical seed + settings = identical reviews.

Privacy note

Reviews are generated locally in your browser and never uploaded. Everything is fictional — no real products, customers, or opinions — and must never be published as genuine feedback. Fabricating real-looking reviews for actual products is both unethical and illegal in most markets; this data is for building and testing software.

FAQ

Why would I raise the minimum rating? To test how your UI and analytics behave on skewed data — a 4.8-average product renders very differently from a 3.2 one, and moderation tools should notice the difference.

Do review bodies match their ratings? The text is plausible filler appropriate for testing display, truncation, and search — treat sentiment-to-star alignment as approximate.

Can I join these to products? Generate a catalog with the Fake Product Dataset tool and key the two sets together in your fixture code — both are seeded, so the join stays stable.