Facebook Reviews Scraper

Extract every review, rating, and recommendation from any public Facebook Page — reviewer details, star rating, recommendation status, review text, likes, comments, and the business's own replies — delivered as clean, ready-to-use JSON, CSV, or Excel.

No login, no setup, no configuration. Paste a Page URL (or just the username) and get structured review data back in seconds.

Why use this scraper

• Most complete field set on the market — reviewer profile, recommendation flag, likes, comments, reply text, and more. Nothing else on Apify returns this much per review.
• Runs on a fraction of the compute other Facebook review scrapers need — which means faster queueing, lower cost per run, and results that land quicker.
• Zero setup. No Facebook login, no API keys, no cookies to paste in — just the Page URL.
• Built for automation. Clean JSON output that drops straight into n8n, Make, Zapier, LangChain, or any AI agent / MCP pipeline.
• Multiple pages in one run, fully concurrent — scrape a competitor set or an entire brand portfolio in a single job.
• Handles both positive recommendations and negative reviews, with full pagination so you're not capped at the first page Facebook shows you.

Input

Field	Type	Description
pageUrls	array of strings	Facebook Page URLs to pull reviews from, e.g. https://www.facebook.com/cocacola
usernames	array of strings	Alternative to full URLs — just the Page username, e.g. cocacola
maxItems	integer	Maximum total reviews to collect across all pages (default: 500)
maxReviewsPerPage	integer	Cap on reviews per individual page. 0 = no per-page cap
proxyConfiguration	object	Optional — only needed in rare edge cases

You can mix pageUrls and usernames in the same run.

Output example

{
  "reviewId": "1234567890123456",
  "reviewerName": "Jane Doe",
  "reviewerId": "100000123456789",
  "reviewerAvatar": "https://scontent.xx.fbcdn.net/v/....jpg",
  "rating": 5,
  "isRecommended": true,
  "text": "Great service and super fast delivery. Would recommend to anyone!",
  "date": "2026-05-14T10:32:00.000Z",
  "likesCount": 12,
  "commentsCount": 3,
  "url": "https://www.facebook.com/cocacola/posts/1234567890123456",
  "tags": [],
  "hasReply": true,
  "replyText": "Thank you so much for your kind words, Jane!",
  "pageName": "Coca-Cola",
  "pageUrl": "https://www.facebook.com/cocacola",
  "scrapedAt": "2026-07-26T08:12:41.000Z"
}

Every field, explained

Field	Description
reviewId	Unique ID of the review/recommendation
reviewerName	Name of the person who left the review
reviewerId	Facebook user ID of the reviewer
reviewerAvatar	URL of the reviewer's profile picture
rating	1–5 star rating (recommendations map to 5 or 1)
isRecommended	true if the reviewer recommends the page, false if not
text	Full text of the review
date	Date/time the review was posted (ISO 8601)
likesCount	Number of likes/reactions on the review
commentsCount	Number of comments on the review
url	Direct link to the review
tags	Any tags attached to the review
hasReply	Whether the business replied
replyText	The business's reply text, if any
pageName	Name of the Facebook Page reviewed
pageUrl	URL of the Facebook Page
scrapedAt	Timestamp of when the data was collected

Use cases

• Reputation monitoring — track new reviews and ratings for your own Page in real time.
• Competitor analysis — see what customers say about competitor businesses, and how (or if) they reply.
• Lead generation — find businesses with poor review-response rates and pitch your reputation-management service.
• Sentiment & NLP pipelines — feed review text straight into a classifier, LLM summarizer, or dashboard.
• Customer service audits — measure reply rate and reply speed across a portfolio of Pages.
• Market research — aggregate ratings and recommendation rates across an entire industry vertical.

API, MCP & integrations

This actor runs on the Apify platform and works with:

• Apify API & client libraries (Python, JavaScript) for programmatic runs
• Webhooks for real-time notifications when a run finishes
• MCP (Model Context Protocol) so AI agents — including Claude, ChatGPT, and custom LLM agents — can call this scraper as a tool
• n8n, Make, and Zapier integrations for no-code automation
• Scheduled runs to monitor a Page's reviews on a recurring basis

Export results as JSON, CSV, Excel, or push directly to Google Sheets.

FAQ

Do I need a Facebook account or login? No. This scraper works entirely on public Page data — no login, no cookies, no account required.

Can I scrape multiple Pages in one run? Yes — add as many pageUrls or usernames as you like; they're processed together in the same run.

Does it get every review, or just the first page? It paginates through all available reviews for each Page, up to the limit you set in maxItems.

What if a Page has no reviews enabled? The run completes cleanly and simply returns zero results for that Page — no errors.

How fresh is the data? Every run scrapes live, current data directly from Facebook at the time you run it.