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Beauty & Skincare Reviews Dataset

Structured beauty and skincare product reviews with star ratings, review text, product and brand details, retailer context, skin concerns, and other category-specific attributes.

Request sample data View data fields

Built for

Beauty brands, cosmetics researchers, retailers, and AI developers

  • ✓Structured review text and ratings
  • ✓CSV or JSON delivery
  • ✓Custom filters when available
  • ✓One-time or recurring collection

What is included in beauty and skincare retailers review data?

The exact schema depends on the collection. These are the commonly available fields to validate in your sample.

review_id
product_id
product_name
brand
retailer
rating
review_title
review_text
review_date
verified_status
skin_type
skin_concern
helpful_votes
language

Explore the dedicated BeautyFeeds collection

For a dedicated beauty-data experience, explore BeautyFeeds for beauty and skincare review datasets, retailer coverage, and category-specific data options.

BeautyFeeds Reviews Dataset

Example normalized record

This anonymized, illustrative record shows the expected shape. Confirm actual fields and values using the sample for your selected collection.

{
  "review_id": "BEAUTY_37D18",
  "product_id": "SKU-20419",
  "product_name": "Example Hydrating Serum",
  "brand": "Example Brand",
  "retailer": "Example Beauty Retailer",
  "rating": 4,
  "review_title": "Hydrating without feeling heavy",
  "review_text": "The texture absorbs quickly and works well under sunscreen.",
  "review_date": "2026-08-14",
  "skin_type": "combination",
  "skin_concern": "dryness",
  "helpful_votes": 12,
  "language": "en"
}

Common use cases

Sentiment analysis

Classify review sentiment and compare rating-to-text agreement.

Topic and aspect extraction

Identify recurring product, service, feature, or support themes.

Competitive intelligence

Compare ratings, review volume, response patterns, and customer concerns.

AI evaluation and training

Develop retrieval, summarization, classification, and language-analysis workflows.

Trend monitoring

Measure how feedback changes by version, business, market, or time period.

Collection and quality considerations

Before purchasing or training a model, verify the collection date, geographic and language coverage, rating distribution, duplicate rate, missing fields, deleted-content policy, and source-specific limitations.

A current snapshot is suitable for present-state analysis. Measuring changes over time requires dated recurring snapshots collected with a consistent method.

Request a targeted extract

Scope the collection by app, business, country, language, rating, or date range when those filters are exposed by beauty and skincare retailers. We can also discuss recurring delivery and a normalized schema.

Discuss your review data requirements

Beauty & Skincare Reviews Dataset FAQ

Common fields include review id, product id, product name, brand, retailer, rating, review title, review text. Exact fields and completeness depend on the selected collection and are documented in its sample.

Depending on the retailer, records may include skin type, skin concern, age range, shade, verified-purchase status, recommendation flags, helpful votes, and product variant details. Confirm exact coverage in the sample.

Yes. You can review representative fields and request sample records for the apps, businesses, markets, languages, or date range relevant to your project.

The rating and text fields can support sentiment classification, topic extraction, summarization, retrieval, and model evaluation, subject to the applicable dataset license.

Yes. Availability depends on the source and scope. Ask for a dated historical snapshot or a recurring collection schedule when requesting access.

Review datasets are commonly delivered in CSV or JSON. Confirm the format, encoding, record count, and delivery method for the selected collection.

Yes, when those fields are available from the source. Custom extracts can be scoped by rating, date range, language, country, app, or business.

Public samples should omit or anonymize unnecessary reviewer identifiers. Review the exact retained fields, privacy treatment, and license before using the full dataset.

Explore more customer review datasets

Compare mobile app, business, product, and hotel review collections from the main reviews hub.

All review datasets Sentiment analysis use case