Use Cases

Review & Sentiment Analysis

Turn millions of structured customer reviews into sentiment signals, product feedback, and trust-verified social proof.

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The challenge

A single popular SKU can accumulate tens of thousands of reviews across just one retailer, and a brand selling through dozens of retailers ends up with review volume no team can read manually. Each retailer also presents review data differently — different rating scales, different optional fields, no consistent way to tell a real customer review from an incentivized one.

Raw review text is also messy for analysis on its own. Sentiment buried in free text is only useful once you can separate signal from noise — a five-star review from a staff account or an incentivized reviewer skews a sentiment read very differently than an organic, verified purchase does, and most retailer review widgets don't make that distinction easy to isolate at scale.

Average star rating alone hides more than it reveals. A 4.2-star product might be loved by dry-skin customers and disliked by oily-skin customers in roughly equal measure — a pattern that never shows up until you can segment sentiment by who actually left the review.

How it helps
How BeautyFeeds helps
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Every review is collected as its own structured record — rating, title, full review_text, and structured pros/cons when the retailer collects them — tied to product_id and sku_id, so it joins directly against our product dataset for per-SKU analysis.

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Trust-signal fields do the filtering work for you before any model runs: is_verified_purchase, incentivized_review, and is_staff let you isolate a clean, organic sentiment signal, while helpful_votes and not_helpful_votes give a community-validated read on which reviews actually resonated with other shoppers.

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Reviewer attributes — skin_type, skin_tone, age_range, skin_concern, eye_color, hair_color — let you segment sentiment by who is reviewing, not just what they said. That turns a flat average rating into a real demographic breakdown of product performance.

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Because reviews are collected alongside product data across 55+ retailers and 7 countries with the same schema, a cross-retailer or cross-market sentiment comparison doesn't require reconciling differently structured sources.

A real slice of the reviews dataset

Six actual rows from our Sephora (US) review collection — unedited ratings and text, showing both praise and criticism.

ProductRatingTitleVerifiedReview
Golden Hour 24HR Skin Tint5—NoThis product exceeds my expectations. It has a smooth finish and has ample coverage which is so nice.
GinZing™ Refreshing Scrub Cleanser4pretty good !Noi have combo skin and this worked great. its exfoliating so it helped with my oily skin but still pretty hydrating.
Sugar Lip Balm Sunscreen SPF 155The most important item in your make-up bagNoLooks great on its own and is a fabulous base for lip gloss and lipstick. The spf 15 makes it absolutely indispensable.
Wink Stamp Long Waterproof Wing Eyeliner2MidYesEh. Stamp didn't look good on me and the eyeliner was fine.
Water Drench Hyaluronic Cloud Cream3Not what I expectedYesI expected more but it's not that nourishing for my very dry skin. If you have very very dry skin then it's not for you.
The Concentrate Serum for Barrier Repair4LoveNoFirst impression was velvety, a silky texture when I applied it to my skin. Looking forward to trying more products.
See it in action
A typical workflow

How this typically plays out for a team using BeautyFeeds data.

Step 1

A data team filters the review set to the retailers, countries, and date range they need, then previews the matching count before buying — narrowing further by rating, verified purchase status, or recommended flag until the slice is exactly what the analysis calls for.

Step 2

review_text is run through a sentiment or topic model, with results cross-tabbed against skin_type, skin_tone, and age_range to find patterns that a flat average rating would never surface — for example, a serum that scores well overall but consistently draws complaints about fragrance from one reviewer segment.

Step 3

Findings feed back into product and marketing decisions: recurring complaints in pros/cons inform reformulation priorities, while consistently strong, verified-purchase reviews become a source of vetted testimonials for marketing use.

Relevant data fields

This use case draws on the full Individual Reviews group in our field reference — rating, title, review_text, pros, cons, is_verified_purchase, incentivized_review, is_staff, helpful_votes, and not_helpful_votes for the core sentiment and trust-filtering work.

skin_type, skin_tone, age_range, skin_concern, eye_color, and hair_color support demographic segmentation, while product_id and sku_id join reviews back to the matching record in our product dataset for SKU-level analysis.

Who uses this

NLP and data science teams building sentiment or topic models, brand insights teams tracking how a product is actually received post-launch, and marketing teams sourcing vetted, verified-purchase testimonials are the most common users of this use case.

Product development and formulation teams also use structured pros/cons data directly — recurring, specific complaints across verified reviews are often a clearer reformulation signal than a slowly drifting average rating.

Getting started

Start with a free 50-row sample to check the data shape and fields against what your analysis needs. From there, filter by site, country, rating, submission date, verified purchase, and recommended status, preview the matching count, and buy exactly that slice rather than an entire undifferentiated review dump.

If you're training a model, it's worth pulling a verified-purchase-only sample first to establish a clean baseline before deciding whether to include unverified or incentivized reviews at all.

Reach out if you're not sure which retailers or countries have enough review volume for your target SKUs — we can help scope the pull before you commit to a purchase.

Common questions

Is review text raw or cleaned? Review text is delivered as collected from the retailer, so standard NLP preprocessing (encoding normalization, HTML stripping) is still worth applying on your end.

Can I filter to verified purchases only? Yes — is_verified_purchase is a standard filter, along with incentivized_review and is_staff, so you can build exactly the trust-filtered set your analysis needs before buying.

How often is new review data collected? Reviews are collected on the same weekly refresh cycle as our product data, so new reviews for tracked products accumulate continuously rather than in a single one-time pull.

Ready to put this data to work?

Tell us about your use case and we'll help you find the right plan and fields.

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