📘 Facebook Reviews Scraper

🚀 Pull reviews from any Facebook page in minutes. Review text, recommendation status, reviewer profile, engagement metrics, page-level stats. No login.

🕒 Last updated: 2026-05-09 · 📊 15+ fields per review · 🔄 Multi-page batch · 🚫 No auth required

Pull structured records from Facebook Reviews — clean fields ready as CSV, JSON, JSONL, Excel, or XML for downstream pipelines.

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Reviews ➸ You are here	Ads Library Political+commercial ad archive	Pages Page profiles + about	Posts Wall posts + reactions

Copy to your AI assistant

Copy this block into ChatGPT, Claude, Cursor, or any LLM to start using this actor.

parseforge/facebook-reviews-scraper on Apify. Call: ApifyClient("TOKEN").actor("parseforge/facebook-reviews-scraper").call(run_input={...}), then client.dataset(run["defaultDatasetId"]).list_items().items for results. Key inputs: startUrls (array, default [{"url": "https://www.facebook.com/McDonalds/"}]), maxItems (integer, default 10), maxReviewsPerPage (integer), proxyConfiguration (object, default {"useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"]}). Full actor spec: fetch build via GET https://api.apify.com/v2/acts/parseforge~facebook-reviews-scraper (Bearer TOKEN). Get token: https://console.apify.com/account/integrations

Pull live reviews from any Facebook public page. The actor accepts a list of Facebook page URLs, walks each page's reviews, and returns one structured record per review ready for reputation management, sentiment analysis, brand monitoring, or competitive review intelligence.

Every run fetches data live so you get the current state of Facebook at run time. Records include the review text, recommendation status (recommended / not recommended), reviewer name, reviewer profile URL, posted date, like count, comment count, page name, page total review count, and a back-reference URL.

👥 Built for	🎯 Primary use cases
Reputation management	Bulk-export Facebook reviews for response workflows
Brand and social listening	Monitor brand sentiment across pages
Restaurant chains	Aggregate reviews across all locations
Hospitality groups	Track guest sentiment by property
Marketing agencies	Build review-aggregation reports for clients
Researchers	Study customer sentiment across industries

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📋 What the Facebook Reviews Scraper does

• 📘 Multi-page batch. Pass multiple page URLs in a single run.
• ⭐ Recommendation status. Captures whether the reviewer recommended the page.
• 👤 Reviewer info. Name, profile URL.
• 📅 Timestamps. Posted date for each review.
• 💬 Engagement metrics. Like and comment counts on the review itself.
• 📊 Page-level stats. Total review count and recommendation rate.

The scraper walks each page's reviews, paginates through all reviews up to maxReviewsPerPage, and pushes structured records to the dataset.

💡 Why it matters: Facebook reviews drive purchase decisions for restaurants, hospitality, retail, and local services but Facebook lacks bulk export. A live, structured pull beats manual scraping for reputation management and competitive intelligence.

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🎬 Full Demo

🚧 Coming soon: a 3-minute walkthrough showing setup, a live run, and how to pipe results into Slack via Apify integrations.

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⚙️ Input

Field	Type	Name	Description
startUrls	array	Facebook Page URLs	Required. Page URLs (e.g. https://www.facebook.com/McDonalds/).
maxItems	integer	Max Items	Free users: limited to 10 items (preview). Paid users: optional, max 1,000,000.
maxReviewsPerPage	integer	Max Reviews Per Page	Cap reviews per page. Leave empty to use maxItems globally.
proxyConfiguration	object	Proxy Configuration	Proxy settings. Defaults to Apify residential pool.

Example 1. Single page reviews.

{
  "startUrls": [{"url": "https://www.facebook.com/McDonalds/"}],
  "maxItems": 50
}

Example 2. Multi-page batch.

{
  "startUrls": [
    {"url": "https://www.facebook.com/Walmart/"},
    {"url": "https://www.facebook.com/Target/"},
    {"url": "https://www.facebook.com/Costco/"}
  ],
  "maxReviewsPerPage": 100,
  "maxItems": 300
}

⚠️ Good to Know: Facebook aggressively rate-limits scraping. Residential proxies are required for reliable pulls.

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📊 Output

The dataset returns one structured record per review. Each record carries identifiers, review text, recommendation status, reviewer info, timestamp, engagement metrics, page name, and a back-reference URL. Consume the dataset as JSON, CSV, Excel, XML, or RSS via the Apify console or API.

🧾 Schema

Field	Type	Example
🆔 reviewId	string	123456789012345_67890
📘 pageName	string	McDonald's
🔗 pageUrl	string (url)	https://www.facebook.com/McDonalds/
📝 text	string	Always great service and quick orders.
⭐ recommends	boolean	true
👤 reviewerName	string	Jane Smith
🔗 reviewerUrl	string (url)	https://www.facebook.com/jane.smith
📅 postedAt	ISO datetime	2026-04-12T14:30:00.000Z
👍 likeCount	number	12
💬 commentCount	number	3
📊 pageRecommendationRate	number	0.87
📊 pageTotalReviews	number	15240
📅 scrapedAt	ISO datetime	2026-05-09T12:00:00.000Z

📦 Sample records

1. Positive review (recommended)

{
  "reviewId": "123456789012345_67890",
  "pageName": "McDonald's",
  "pageUrl": "https://www.facebook.com/McDonalds/",
  "text": "Always great service and quick orders. Staff is friendly.",
  "recommends": true,
  "reviewerName": "Jane Smith",
  "reviewerUrl": "https://www.facebook.com/jane.smith",
  "postedAt": "2026-04-12T14:30:00.000Z",
  "likeCount": 12,
  "commentCount": 3,
  "pageRecommendationRate": 0.87,
  "pageTotalReviews": 15240,
  "scrapedAt": "2026-05-09T12:00:00.000Z"
}

2. Negative review (not recommended)

{
  "reviewId": "234567890123456_78901",
  "pageName": "Walmart",
  "pageUrl": "https://www.facebook.com/Walmart/",
  "text": "Long lines and items I needed were out of stock.",
  "recommends": false,
  "reviewerName": "John Doe",
  "reviewerUrl": "https://www.facebook.com/john.doe",
  "postedAt": "2026-05-01T09:00:00.000Z",
  "likeCount": 0,
  "commentCount": 1,
  "pageRecommendationRate": 0.79,
  "pageTotalReviews": 28500,
  "scrapedAt": "2026-05-09T12:00:00.000Z"
}

3. Sparse record (no engagement)

{
  "reviewId": "345678901234567_89012",
  "pageName": "Local Cafe",
  "pageUrl": "https://www.facebook.com/local-cafe/",
  "text": "Decent coffee.",
  "recommends": true,
  "reviewerName": "Anon User",
  "postedAt": "2026-05-08T10:00:00.000Z",
  "likeCount": 0,
  "commentCount": 0,
  "scrapedAt": "2026-05-09T12:00:00.000Z"
}

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✨ Why choose this Actor

	Capability
🎯	Built for the job. Scoped specifically to Facebook reviews so you skip the parser engineering entirely.
🔖	Structured output. Clean, typed fields ready for analysis, dashboards, or downstream pipelines.
⚡	Fast. Optimized request patterns return results in seconds, not minutes.
🔁	Always fresh. Every run pulls live data, so the dataset reflects Facebook as of run time.
🌐	No infra to manage. Apify handles proxies, retries, scaling, scheduling, and storage.
🛡️	Reliable. Battle-tested across many runs and edge cases, with graceful error handling.
🚫	No code required. Configure in the UI, run from CLI, schedule via cron, or call from any language with the Apify SDK.

📊 Production-grade structured review data without the engineering overhead of building and maintaining your own scraper.

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📈 How it compares to alternatives

Approach	Cost	Coverage	Refresh	Filters	Setup
⭐ Facebook Reviews Scraper (this Actor)	$5 free credit, then pay-per-use	Public Facebook pages	Live per run	Page URL, max-per-page	⚡ 2 min
Build your own scraper	Engineering hours	Full once built	Whenever you maintain it	Custom code	🐢 Days to weeks
Paid reputation-management tools	$$$ monthly per location	Vendor-defined	Periodic	Vendor-defined	⏳ Hours
Manual sourcing	Hours per check	Limited	Stale	Manual	🕒 Variable

Pick this Actor when you want broad coverage, source-native filtering, and no pipeline maintenance.

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🚀 How to use

1. 📝 Sign up. Create a free account with $5 credit (takes 2 minutes).
2. 🌐 Open the Actor. Go to the Facebook Reviews Scraper page on the Apify Store.
3. 🎯 Add page URLs. Paste Facebook page URLs into startUrls and configure cap.
4. 🚀 Run it. Click Start and let the Actor collect your data.
5. 📥 Download. Grab your results in the Dataset tab as CSV, Excel, JSON, or XML.

⏱️ Total time from signup to downloaded dataset: 3-5 minutes. No coding required.

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💼 Business use cases

📊 Reputation management Bulk-export Facebook reviews for response workflows Build review-aggregation dashboards Track recommendation rate over time Power crisis-response monitoring	🏢 Brand and social listening Monitor brand sentiment across pages Track competitor review activity Build crisis-detection dashboards Map share-of-voice in your industry
🎯 Multi-location operations Aggregate reviews across all locations Build location-level CSAT scorecards Surface underperforming locations Power chain-wide quality programs	🛠️ Engineering and product Power review-aggregation products Replace fragile in-house Facebook scrapers Wire datasets into your apps via the Apify API or webhooks Skip the proxy, retry, and parsing maintenance entirely

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🌟 Beyond business use cases

Data like this powers more than commercial workflows. The same structured records support research, education, civic projects, and personal initiatives.

🎓 Research and academia Empirical datasets for papers, thesis work, and coursework Longitudinal studies tracking changes across snapshots Reproducible research with cited, versioned data pulls Classroom exercises on data analysis and ethical scraping	🎨 Personal and creative Side projects, portfolio demos, and indie app launches Data visualizations, dashboards, and infographics Content research for bloggers, YouTubers, and podcasters Hobbyist collections and personal trackers
🤝 Non-profit and civic Transparency reporting and accountability projects Advocacy campaigns backed by public-interest data Community-run databases for local issues Investigative journalism on public records	🧪 Experimentation Prototype AI and machine-learning pipelines with real data Validate product-market hypotheses before engineering spend Train small domain-specific models on niche corpora Test dashboard concepts with live input

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🔌 Automating Facebook Reviews Scraper

This Actor exposes a REST endpoint, so you can drive it from any language or workflow tool.

• Node.js - call it via the Apify JS SDK.
• Python - call it via the Apify Python SDK.
• REST - hit it directly through the Apify v2 API.

Schedules. Use Apify Scheduler to capture daily review snapshots. Combine with the Apify dataset diff tools to alert on new reviews and sentiment shifts.

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❓ Frequently Asked Questions

🔌 Integrate with any app

Facebook Reviews Scraper connects to any cloud service via Apify integrations:

• Make - Automate multi-step workflows
• Zapier - Connect with 5,000+ apps
• Slack - Get run notifications in your channels
• Airbyte - Pipe results into your warehouse
• GitHub - Trigger runs from commits and releases
• Google Drive - Export datasets straight to Sheets

You can also use webhooks to trigger downstream actions when a run finishes.

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🔗 Recommended Actors

• 📘 Facebook Posts Scraper - Page posts with engagement
• 📘 Facebook Search Scraper - Facebook search results
• 📸 Instagram Posts Scraper - Instagram posts with metadata
• 📱 Reddit Posts Scraper - Subreddit posts
• 🐦 X (Twitter) Scraper - Tweets with engagement

💡 Pro Tip: browse the complete ParseForge collection for more reference-data scrapers.

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🆘 Need Help? Open our contact form to request a new scraper, propose a custom project, or report an issue.

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⚠️ Disclaimer. This Actor is an independent tool and is not affiliated with, endorsed by, or sponsored by Meta or Facebook. All trademarks mentioned are the property of their respective owners. The scraper accesses only publicly available pages and is intended for legitimate research, analytics, and reputation-management use. Users are responsible for compliance with Facebook's Terms of Service, applicable privacy laws, and any data-protection rules that apply.