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Sephora Reviews API

Scrape Sephora Reviews data with one API call. Returns written customer reviews for a Sephora product, 10 per page by default, each with the full review text, star rating, title, reviewer nickname, submission date, and helpful-vote count. The review total is exact. Pass the returned cursor back until has_more is false. Optional sort_by: most_helpful, highest_rating, lowest_rating, oldest, newest. Optional rating keeps only that star value.

Last updated October 2026Maintained by the SocialCrawl team

Start for freeView Documentation

Returns written customer reviews for a Sephora product, 10 per page, each with text, star rating, title, reviewer nickname, and submission date.

Use it when you have a product id and need the review text. The star average is already on GET /v1/sephora/product.

Searching 68 platforms in parallel

·TikTok logoTikTok·Instagram logoInstagram·YouTube logoYouTube·Facebook logoFacebook·X logoX·LinkedIn logoLinkedIn·Reddit logoReddit·Threads logoThreads·Pinterest logoPinterest·Twitch logoTwitch·Truth Social logoTruth Social·Snapchat logoSnapchat·Kick logoKick·Bluesky logoBluesky·Kwai logoKwai·Rumble logoRumble·Spotify logoSpotify·Apple Music logoApple Music·TikTok Shop logoTikTok Shop·Amazon Shop logoAmazon Shop·Google Shopping logoGoogle Shopping·Trustpilot logoTrustpilot·TripAdvisor logoTripAdvisor·Yelp logoYelp·Linktree logoLinktree·Komi logoKomi·Pillar logoPillar·lnk.bio logolnk.bio·Facebook Ads logoFacebook Ads·Google Ads logoGoogle Ads·LinkedIn Ads logoLinkedIn Ads·Google Search logoGoogle Search·Google News logoGoogle News·Finance logoFinance·Polymarket logoPolymarket·Tavily logoTavily·Hacker News logoHacker News·GitHub logoGitHub·Perplexity logoPerplexity·Naver logoNaver·Utility logoUtility·Universal Search logoUniversal Search
·TikTok logoTikTok·Instagram logoInstagram·YouTube logoYouTube·Facebook logoFacebook·X logoX·LinkedIn logoLinkedIn·Reddit logoReddit·Threads logoThreads·Pinterest logoPinterest·Twitch logoTwitch·Truth Social logoTruth Social·Snapchat logoSnapchat·Kick logoKick·Bluesky logoBluesky·Kwai logoKwai·Rumble logoRumble·Spotify logoSpotify·Apple Music logoApple Music·TikTok Shop logoTikTok Shop·Amazon Shop logoAmazon Shop·Google Shopping logoGoogle Shopping·Trustpilot logoTrustpilot·TripAdvisor logoTripAdvisor·Yelp logoYelp·Linktree logoLinktree·Komi logoKomi·Pillar logoPillar·lnk.bio logolnk.bio·Facebook Ads logoFacebook Ads·Google Ads logoGoogle Ads·LinkedIn Ads logoLinkedIn Ads·Google Search logoGoogle Search·Google News logoGoogle News·Finance logoFinance·Polymarket logoPolymarket·Tavily logoTavily·Hacker News logoHacker News·GitHub logoGitHub·Perplexity logoPerplexity·Naver logoNaver·Utility logoUtility·Universal Search logoUniversal Search
·TikTok logoTikTok·Instagram logoInstagram·YouTube logoYouTube·Facebook logoFacebook·X logoX·LinkedIn logoLinkedIn·Reddit logoReddit·Threads logoThreads·Pinterest logoPinterest·Twitch logoTwitch·Truth Social logoTruth Social·Snapchat logoSnapchat·Kick logoKick·Bluesky logoBluesky·Kwai logoKwai·Rumble logoRumble·Spotify logoSpotify·Apple Music logoApple Music·TikTok Shop logoTikTok Shop·Amazon Shop logoAmazon Shop·Google Shopping logoGoogle Shopping·Trustpilot logoTrustpilot·TripAdvisor logoTripAdvisor·Yelp logoYelp·Linktree logoLinktree·Komi logoKomi·Pillar logoPillar·lnk.bio logolnk.bio·Facebook Ads logoFacebook Ads·Google Ads logoGoogle Ads·LinkedIn Ads logoLinkedIn Ads·Google Search logoGoogle Search·Google News logoGoogle News·Finance logoFinance·Polymarket logoPolymarket·Tavily logoTavily·Hacker News logoHacker News·GitHub logoGitHub·Perplexity logoPerplexity·Naver logoNaver·Utility logoUtility·Universal Search logoUniversal Search
·TikTok logoTikTok·Instagram logoInstagram·YouTube logoYouTube·Facebook logoFacebook·X logoX·LinkedIn logoLinkedIn·Reddit logoReddit·Threads logoThreads·Pinterest logoPinterest·Twitch logoTwitch·Truth Social logoTruth Social·Snapchat logoSnapchat·Kick logoKick·Bluesky logoBluesky·Kwai logoKwai·Rumble logoRumble·Spotify logoSpotify·Apple Music logoApple Music·TikTok Shop logoTikTok Shop·Amazon Shop logoAmazon Shop·Google Shopping logoGoogle Shopping·Trustpilot logoTrustpilot·TripAdvisor logoTripAdvisor·Yelp logoYelp·Linktree logoLinktree·Komi logoKomi·Pillar logoPillar·lnk.bio logolnk.bio·Facebook Ads logoFacebook Ads·Google Ads logoGoogle Ads·LinkedIn Ads logoLinkedIn Ads·Google Search logoGoogle Search·Google News logoGoogle News·Finance logoFinance·Polymarket logoPolymarket·Tavily logoTavily·Hacker News logoHacker News·GitHub logoGitHub·Perplexity logoPerplexity·Naver logoNaver·Utility logoUtility·Universal Search logoUniversal Search
Sephora API

What can you do with the Reviews API?

The Reviews endpoint gives you structured Sephora data with computed fields in a single request. No scraping infrastructure to build or maintain.

Example Request

curl -H "x-api-key: YOUR_API_KEY" \
  "https://www.socialcrawl.dev/v1/sephora/reviews?product_id=P427414&label=issue"
import requests

response = requests.get(
    "https://www.socialcrawl.dev/v1/sephora/reviews",
    params={
    'product_id': 'P427414',
    'label': 'issue',
    },
    headers={"x-api-key": "YOUR_API_KEY"},
)

data = response.json()
const response = await fetch(
  "https://www.socialcrawl.dev/v1/sephora/reviews?product_id=P427414&label=issue",
  {
    headers: { "x-api-key": "YOUR_API_KEY" },
  },
);

const data = await response.json();

Parameters worth sending

  • label=issue Names the product issue each review raises, at no extra credits; sentiment is free too.

Parameters

ParameterRequiredDescription
product_idNoSephora product id, the P-number at the end of a product URL: sephora.com/product/the-ordinary-deciem-natural-moisturizing-factors-ha-P427414 is product id P427414. Product ids also come back on every row of GET /v1/sephora/search and GET /v1/sephora/category.
urlNoFull Sephora product URL, for example https://www.sephora.com/product/the-ordinary-deciem-natural-moisturizing-factors-ha-P427414. The product id is extracted from the path.
pageNoPage number, starting at 1. Prefer the universal cursor parameter.
limitNoReviews per page, 1 to 100. Default 10.
sort_byNoReview ordering: most_helpful, highest_rating, lowest_rating, oldest, newest. (most_helpful | highest_rating | lowest_rating | oldest | newest)
ratingNoKeep only reviews with this star value, 1 to 5.
labelNoOptional CSV of SocialCrawl labels to add to every review. Without this param every page already carries sentiment and issue, free; label= adds the labels you name to them (the defaults keep running). sentiment and issue are free when asked for too; reports, incentivized and injection add 1 credit per started 25 newly judged reviews. judgments=off (or label=none) turns the default labels off. They read the review's title and text, never its stars. sentiment: how the reviewer feels, on five levels (level 0 to 4, score_0_1, confidence), plus rating_mismatch, true when the words clearly contradict the star rating (a glowing text under 1 star, a furious one under 5), null when the review has no rating. issue: the main problem the review reports (label: product_defect, sizing_or_fit, shipping_or_delivery, customer_service, price_or_value, missing_feature, other, or none, with confidence; label is null when unsure). reports (needs reports=): does this review say that the thing you describe happened (p, 0 to 1). incentivized: does the reviewer say they got the product free, discounted or rewarded for the review (p), and did the text carry a disclosure such as 'in exchange for my honest review', Vine or 체험단 (disclosed). injection flags text that addresses an AI system and tries to direct it (flagged, p); it never drops or rewrites a row. These are signals to read, never a verdict on a review or a reviewer. A review that could not be judged carries labels: null. Reviews already labelled for anyone are free, and so is a cached page. data.labels reports what was judged and billed.
reportsNoRequired by label=reports, ignored otherwise. What to look for, in plain words, up to 300 characters, for example reports=the battery drains quickly or reports=배송이 늦음. Every review gains labels.reports.p, the probability that it says so, in any wording or language. Without it label=reports is skipped with the warning label_reports_needs_reports and is not billed.
judgmentsNoOptional, on (the default) or off. By default every row gains free SocialCrawl judgments (computed.labels, and computed.relevance on search endpoints), reported in data.labels (mode default) and data.relevance (origin default), each with a status (complete, partial or skipped) and pending: the rows still being judged when the page was sent, which carry null now and are filled on your next call or cached read. Default judgments never add credits, never change an existing field, and never drop or reorder a row. off returns the page exactly as before, with none of those keys. label=none does the same. (on | off)
dry_runNoOptional. When 1, return a cost preview for this labelled or relevance-filtered request without fetching the page or judging any row. data.estimate reports rows_expected, rows_cached, label_credits_min, label_credits_max and base_credits. 0 credits charged. (1)
label_evidenceNoOptional, only with label=. When 1, every labelled row also carries computed.labels_evidence.<preset> = { quote, sentence_index }: the sentence in the row that most clearly shows the label, copied verbatim. Absent or null when no single sentence shows it. (1)
fitNoOptional. When goal, keep the rows and fields needed for the goal you pass in goal= (plus any that are uncertain, and the first and last), and replace the rest with a stub. data.held_back lists the held ids and a recall id that re-reads the full page from cache at no extra charge. Without this param the page is unchanged. (goal)
goalNoRequired by fit=goal. What you are trying to do, in your own words, up to 300 characters.
fit_tokensNoOptional, only with fit=goal. Soft cap on how much of the page to keep, in tokens. Uncertain blocks and the first and last block are kept even if they exceed it.
sinceNoOnly rows published on or after this date: YYYY-MM-DD (midnight UTC) or an ISO 8601 timestamp. Rows older than it are left off each page. This list is not in date order, so the walk does not stop at the date: keep following `next_cursor`, and each page costs what a page costs even when every row on it is filtered out. A row with no date is kept. `published_after`, `start_date`, `from_date`, `publish_time` (`yesterday`, `this-week`, `this-month`, `last-3-months`, `last-6-months`) and `max_age_days` are accepted and applied as `since`.
untilNoOnly rows published on or before this date: YYYY-MM-DD (that whole UTC day is included) or an ISO 8601 timestamp. Newer rows are left off each page and the walk does not stop, so older pages follow. A row with no date is kept. `published_before`, `end_date` and `to_date` are accepted and applied as `until`.
Example Response

What does the Sephora Reviews API return?

Every response follows one unified schema. Here is a real, unmodified response body, so you can see the exact fields you get back before spending a credit.

Example response
{
  "success": true,
  "platform": "sephora",
  "endpoint": "/v1/sephora/reviews",
  "data": {
    "items": [
      {
        "review": {
          "id": "403166879",
          "entity_id": "P427414",
          "url": null,
          "title": "Sample comment text (redacted).",
          "text": "Sample comment text (redacted).",
          "rating": {
            "value": 5,
            "max": 5
          },
          "author": {
            "name": "user_32605d",
            "avatar_url": null,
            "url": null,
            "location": null,
            "reviews_count": null
          },
          "helpful_votes": 0,
          "verified": true,
          "source": null,
          "language": null,
          "original_language": null,
          "translated": null,
          "images": null,
          "responses": null,
          "published_at": "2026-09-15T01:34:42.000Z"
        }
      },
      {
        "review": {
          "id": "403091985",
          "entity_id": "P427414",
          "url": null,
          "title": "Sample comment text (redacted).",
          "text": "Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted).",
          "rating": {
            "value": 2,
            "max": 5
          },
          "author": {
            "name": "user_bf9db4",
            "avatar_url": null,
            "url": null,
            "location": null,
            "reviews_count": null
          },
          "helpful_votes": 0,
          "verified": null,
          "source": null,
          "language": null,
          "original_language": null,
          "translated": null,
          "images": null,
          "responses": null,
          "published_at": "2026-09-14T04:37:03.000Z"
        }
      }
    ],
    "next_cursor": "2",
    "total": 2847,
    "dropped": 0
  },
  "credits_used": 5,
  "request_id": "req_example000000",
  "cached": false,
  "pagination": {
    "next_cursor": "sc.eyJ2IjoyLCJjIjoiMiIsImUiOiJzZXBob3JhL3Jldmlld3MiLCJwIjoicGFnZSJ9",
    "has_more": true,
    "page_size": 10
  },
  "credits_remaining": 9999,
  "redacted": true
}

Live sample illustrating the unified response shape. Field values reflect the record you query.

API Details

How does the Sephora Reviews API work?

Send a GET request with your API key and get back clean, structured JSON in our unified schema. Supported computed fields are populated when the source provides the required inputs.

Method

GET

Response

JSON

Why SocialCrawl

Why use SocialCrawl for Sephora Reviews data?

We handle the complexity of Sephora data extraction so you can focus on building. Unified schema, AI enrichment, and zero platform logic in your code.

Developer First

How do you scrape social media data in seconds?

The fastest social media scraping API for developers. Scrape profiles, posts, comments, and analytics from 68 platforms covering 10B+ monthly active users.

One schema, every platform

Query 68 platforms with identical response structures. Write your integration once.

Computed fields, not just scraped

When an endpoint supports these metrics and the source provides the required inputs, the normalized record includes engagement_rate, estimated_reach, content_category, and language. Ready to use.

See your data before you code

Visual Data Explorer. Paste any URL, get rich result cards, sortable tables, CSV export.

import requests

response = requests.get(
    'https://www.socialcrawl.dev/v1/tiktok/profile',
    params={'handle': 'charlidamelio'},
    headers={'x-api-key': 'sc_YOUR_API_KEY'}
)
data = response.json()
[ .JSON ]
{
  "success": true,
  "platform": "tiktok",
  "data": {
    "author": {
      "username": "charlidamelio",
      "followers": 152400000
    },
    "engagement": {
      "likes": 12400000000,
      "engagement_rate": 0.087
    },
    "metadata": {
      "language": "en",
      "content_category": "lifestyle"
    }
  }
}
+ 68 platforms

Ready to scrape Sephora Reviews data?

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