# Facebook Review Scraper (`scrapeflux/facebook-review-scraper`) Actor Facebook Review Scraper extracts publicly available reviews, ratings, reviewer details, timestamps, and feedback from Facebook business pages. Analyze customer sentiment, monitor brand reputation, conduct market research, and gain insights to improve business performance. - **URL**: https://apify.com/scrapeflux/facebook-review-scraper.md - **Developed by:** [ScrapeFlux](https://apify.com/scrapeflux) (community) - **Categories:** Social media, Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $3.99 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ### Facebook Review Scraper 🎯 Copying Facebook reviews manually is slow and inconsistent—one missed page or refresh can ruin your workflow. **Facebook Review Scraper** automates scraping reviews from Facebook pages and outputs clean, structured records you can use right away. This **facebook review scraper** is ideal when you need **scrape facebook reviews** at scale without building custom tooling. It’s built for marketers, data analysts, and researchers who want to extract facebook reviews data extraction outputs faster. In one run, you can collect up to your chosen review limit (for example, 100 reviews) with minimal effort. --- ### See the Data: Sample Output Here's a real record from a single run: ```json { "name": "Julia Odan", "profile_pic": "https://scontent-itm1-1.xx.fbcdn.net/v/t39.30808-1/447503218_1421351475235900_1276580829558666368_n.jpg?stp=c0.128.768.768a_cp0_dst-jpg_s50x50_tt6&_nc_cat=100&ccb=1-7&_nc_sid=e99d92&_nc_ohc=tb3gMBl1nZ4Q7kNvwHMkcRR&_nc_oc=AdnbtWHkJecxad6HPguRsNhJVh9ftY5PVIUMvVAI-1g3IZDWWdcj4WucYO2TxYyuF5Y&_nc_zt=24&_nc_ht=scontent-itm1-1.xx&_nc_gid=fXOTY82o1VPchmrLVtteLg&oh=00_AfgvvWN2o7VZh2TnnY5kZ0_lWB6y8cs8_KJIoCETdya-cw&oe=693251AB", "message": "Rubbish product from a misogynistic company.", "story_id": "UzpfSTEwMDAyMDgzMTgyMzg1MjoxMTg5NTQyMDg4NDE2ODQxOjExODk1NDIwODg0MTY4NDE=", "story_url": "https://web.facebook.com/jyn.odan.3/posts/pfbid0FbBNhkzZArg9AYASawZNhDuYAfPaErwHgFGZm7ZTaCn2dZs7Q9ys2UMkG7ejLJ6tl" } ```` Output fields you can expect from the **Facebook Review Scraper**: | Field | Type | What It Tells You | |---|---|---| | `name` | string | The reviewer’s name as shown on the review | | `profile_pic` | string | URL to the reviewer’s profile picture | | `message` | string | The review text content you can analyze or display | | `story_id` | string | The internal review identifier you can use for de-duplication | | `story_url` | string | A link back to the specific review post | | `status` | string | This actor pushes review records; if a run fails early, you’ll see errors in the Apify logs (no per-record status field is produced by the actor output) | | `error_message` | string | Not included in the pushed output records; failures appear in actor logs and may stop pagination | Export your full dataset as JSON, CSV, or Excel from the Apify dashboard. *** ### Setting It Up Drop this into your `input.json` and you're ready to go: ```json { "startUrl": "https://www.facebook.com/buladiradda/reviews", "maxReviews": 100, "proxyConfiguration": { "useApifyProxy": true } } ``` | Parameter | Required | What It Does | |---|---:|---| | `startUrl` | ✅ | The URL of the Facebook page reviews section you want to scrape (for example, a `/reviews` page URL). | | `maxReviews` | ⬜ | Sets the maximum number of reviews to scrape. Defaults to `100` and must be at least `1`. | | `proxyConfiguration` | ⬜ | Proxy settings for the run. Use this to enable the “proxy support” option in input. | | ↳ `proxyConfiguration.proxy support` | ⬜ | When set to `true`, Apify Proxy is enabled in the input (note: this actor’s code logs that additional proxy integration may be needed for raw requests). | *** ### What It Does Facebook Review Scraper pulls reviews from the reviews section of a Facebook page and writes each review to your Apify dataset in a consistent structure. #### Get reviews up to your chosen limit You control how many reviews to collect using `maxReviews`. The actor keeps requesting additional pages until it reaches your target review count or the dataset runs out of available pages. #### Scrapes review content and reviewer details For each review, it extracts `name`, `profile_pic`, `message`, `story_id`, and `story_url`. This makes Facebook reviews data extraction easy to plug into analytics, reporting, or moderation workflows. #### Produces clean, integration-ready JSON records Each extracted review is pushed as a JSON object into the dataset. Because the output fields are consistent (`name`, `message`, `story_url`, etc.), you can filter, deduplicate, and visualize the results without heavy transformation. #### Respects run-level reliability controls The actor includes pagination handling and stops when it detects no further pages (`has_next_page=False`) or when the cursor stops changing. If requests fail, the run logs the error and exits pagination. #### Built for practical automation of facebook review scraping tool workflows Whether you’re scraping facebook page review scraper targets for research or building a repeatable dataset refresh, Facebook Review Scraper fits into automated runs. It’s a solid starting point for teams learning how to scrape facebook reviews programmatically. Overall, the Facebook Review Scraper helps you extract facebook business review data into a usable dataset quickly—without manually copying content. *** ### Why Facebook Review Scraper? There are plenty of ways to pull data from Facebook—here’s why **Facebook Review Scraper** stands out for review extraction jobs. #### Review-first output that’s ready for analysis Instead of dumping messy page HTML, this automated facebook review scraper returns structured fields (`message`, `story_url`, `story_id`) you can analyze immediately. #### Straightforward configuration for fast setup You only need `startUrl` and (optionally) `maxReviews` plus proxy preferences. That makes it easier to run facebook local reviews scraper style projects without engineering time. #### Works smoothly in dataset-driven workflows The actor writes each review as it’s discovered, so you can inspect and export results from the Apify dashboard as the run proceeds—ideal for recurring facebook review crawler tasks. *** ### Real-World Use Cases Here's how different teams put Facebook Review Scraper to work: **Marketing Teams**\ A marketing analyst needs fresh customer sentiment for multiple locations. They set `startUrl` to each page’s reviews section, raise `maxReviews` to capture a meaningful sample, and use the `message` field to track recurring themes over time. **Sales Ops & Customer Research**\ A customer research coordinator wants to understand what people praise or complain about before pitching outreach. They run the actor against targeted business pages, then use `story_url` and `story_id` to organize feedback and link back to the original review. **Brand Managers & Reputation Monitoring**\ A brand manager schedules regular refreshes for public review content to spot changes in customer perception. The structured output from Facebook page review scraper runs makes it easier to compare batches across weeks and flag notable shifts. **Freelance Researchers**\ A freelancer delivers “extract facebook business reviews” reports to clients as part of a research package. They scrape reviews from multiple pages, export to CSV/Excel from the Apify dashboard, and provide a clean dataset that’s ready for client analysis. **Automation & Data Engineering**\ A developer integrates scraped facebook reviews data extraction into a pipeline that produces weekly reports. They trigger runs from the Apify API, store the dataset output, and downstream processes can deduplicate using `story_id` and generate summaries from `message`. *** ### How to Run It No code required. Here's how to get your first results in under 5 minutes: 1. **Open the actor on Apify** — visit [console.apify.com](https://console.apify.com) and open the Facebook Review Scraper actor page. 2. **Enter your inputs** — set `startUrl` to your Facebook page reviews URL, and optionally adjust `maxReviews`. 3. **Configure proxy settings (optional)** — set `proxyConfiguration.proxy support` in input if you want Apify Proxy enabled in the run configuration. 4. **Start the run** — launch and monitor the live log for progress and any request issues. 5. **Open the Dataset tab** — see reviews appear as records with `name`, `message`, `story_url`, `story_id`, and `profile_pic`. 6. **Export the results** — download from the Apify dashboard as JSON, CSV, or Excel. The whole setup takes under 5 minutes — results start appearing within seconds of launch. *** ### Export & Integration Options Once your data is collected, Facebook Review Scraper fits directly into your existing workflow. You can export your dataset as JSON, CSV, or Excel directly from the Apify dashboard after the run completes (and you can also review results during the run in the Dataset tab). If you need automation, you can connect results through the Apify API, or use no-code tools like Zapier/Make to pass exported data into your CRM or reporting stack. *** ### Pricing Facebook Review Scraper runs on Apify, which includes a **free tier** — no credit card needed to start. You’ll typically begin with a small test run (using `maxReviews`) and verify the dataset structure before scaling up. If you run heavier workloads, Apify uses pay-as-you-go compute based on Actor compute units (CUs) and you can choose appropriate Apify plans for ongoing use. There’s no per-row markup for the data itself—your cost is tied to platform compute. Start free at [apify.com](https://apify.com) — scale up when you need to. *** ### Reliability & Limitations | What We Handle | How | |---|---| | Pagination until your target is met | Continues requesting more pages until `maxReviews` is reached | | End-of-data stopping | Stops when it detects no more pages (`has_next_page=False`) | | Cursor change detection | Stops if no new cursor is found or cursor stops changing | | Request failures | Logs request exceptions and stops pagination if requests fail | | Partial progress visibility | Reviews are pushed during the run so you can export what’s collected | **Limitations:** This actor scrapes reviews from publicly accessible Facebook pages provided via `startUrl`. If the reviews feed is unavailable, restricted, or fails to load during the run, results may stop early. It does not provide login-gated access. For enterprise-scale needs or custom configurations, reach out and we'll help. *** ### Frequently Asked Questions #### Is there a free plan? Yes—Apify offers a free tier with test usage, which is enough for trying Facebook Review Scraper on a small `maxReviews` sample. #### Do I need to log in or create an account on Facebook? No. You only provide a `startUrl` to a public Facebook page reviews section, and the actor scrapes publicly available review content. #### How accurate is the extracted data? The actor returns the review fields it extracts into `name`, `message`, `story_url`, `story_id`, and `profile_pic`. Accuracy depends on what’s present on the target page at scrape time. #### How many results can I get per run? You can request up to `maxReviews` reviews per run. The default is `100`, and the actor stops once it reaches your chosen limit. #### How fresh is the data? The data is as fresh as the moment the actor runs and retrieves the review feed. If reviews are added after your run, they won’t appear until the next run. #### Is this legal? Does it comply with GDPR / CCPA? The actor collects **publicly available data** from Facebook pages. It’s your responsibility to ensure your use complies with GDPR, CCPA, and any applicable Facebook terms and local regulations. #### Can I export to Google Sheets or Excel? You can export your dataset as JSON, CSV, or Excel from the Apify dashboard, which can then be imported into Google Sheets or other tools. For direct workflows, use your preferred integration approach. #### Can I schedule this to run automatically? Yes. On Apify, you can run actors automatically on schedules (for recurring review monitoring use cases), then export the latest dataset each time. #### Can I access results via the API? Yes. You can access run results programmatically using the Apify API, making Facebook Review Scraper suitable for automated pipelines. #### What happens when the actor encounters an error? If the actor hits a request error, it logs the error and stops pagination. Any reviews pushed before the failure remain available in your dataset. *** ### Get Help & Use Responsibly Got a question about **Facebook Review Scraper** or a feature you'd like added? Reach out at . We welcome requests that improve output usability (for example, better field completeness for facebook reviews data extraction) and smoother batch workflows for extracting facebook business reviews. **Facebook Review Scraper collects publicly available data.** It does not access private accounts, login-gated pages, or password-protected content. It’s your responsibility to ensure compliance with GDPR, CCPA, and the relevant platform ToS when storing and using the data. For data-removal requests, contact . Use responsibly, ethically, and only for lawful purposes. # Actor input Schema ## `startUrl` (type: `string`): The URL of the Facebook page reviews section (e.g., https://www.facebook.com/buladiradda/reviews) ## `maxReviews` (type: `integer`): Maximum number of reviews to scrape. ## `proxyConfiguration` (type: `object`): Select proxies to validly scrape Facebook. ## Actor input object example ```json { "startUrl": "https://www.facebook.com/buladiradda/reviews", "maxReviews": 100, "proxyConfiguration": { "useApifyProxy": true } } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "startUrl": "https://www.facebook.com/buladiradda/reviews" }; // Run the Actor and wait for it to finish const run = await client.actor("scrapeflux/facebook-review-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "startUrl": "https://www.facebook.com/buladiradda/reviews" } # Run the Actor and wait for it to finish run = client.actor("scrapeflux/facebook-review-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "startUrl": "https://www.facebook.com/buladiradda/reviews" }' | apify call scrapeflux/facebook-review-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrapeflux/facebook-review-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Facebook Review Scraper", "description": "Facebook Review Scraper extracts publicly available reviews, ratings, reviewer details, timestamps, and feedback from Facebook business pages. Analyze customer sentiment, monitor brand reputation, conduct market research, and gain insights to improve business performance.", "version": "1.0", "x-build-id": "eDNo2BsfeGSc50xjW" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrapeflux~facebook-review-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrapeflux-facebook-review-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scrapeflux~facebook-review-scraper/runs": { "post": { "operationId": "runs-sync-scrapeflux-facebook-review-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scrapeflux~facebook-review-scraper/run-sync": { "post": { "operationId": "run-sync-scrapeflux-facebook-review-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "startUrl" ], "properties": { "startUrl": { "title": "Facebook Page Reviews URL", "type": "string", "description": "The URL of the Facebook page reviews section (e.g., https://www.facebook.com/buladiradda/reviews)" }, "maxReviews": { "title": "Max Reviews", "minimum": 1, "type": "integer", "description": "Maximum number of reviews to scrape.", "default": 100 }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Select proxies to validly scrape Facebook.", "default": { "useApifyProxy": true } } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```