How to Scrape Facebook Reviews

Scraping Facebook reviews gives you direct access to a wealth of customer opinions you can use to improve your products, marketing, and operations. This guide walks you through why you should collect this data and breaks down four distinct methods for doing it, from a simple copy-paste to more automated techniques. You'll learn the pros and cons of each approach so you can pick the one that fits your needs and technical skill level.

Why Bother Scraping Facebook Reviews?

Your Facebook reviews and recommendations are more than just a star rating, they're a direct line to your customers' thoughts. Manually reading through them is one thing, but collecting them into a single dataset unlocks deeper insights. This process, known as scraping, allows you to analyze feedback much more efficiently.

Here's what you can do with that collected data:

• Uncover Product Feedback: Are customers consistently praising a specific feature or complaining about the same problem? Aggregated reviews make it easy to spot trends you might miss by reading reviews one by one. You can use this intel to guide your product development, Trello, or bug-fixing sprints.
• Supercharge Your Marketing Content: Customer reviews are a goldmine for social proof. You can pull out powerful testimonials to use on your website, in ad campaigns, or on your other social profiles. By analyzing the exact language your customers use, you can also write copy and marketing messages that resonate better with them because it speaks their language.
• Perform Competitor Analysis: This isn't just for your own page. You can scrape the reviews of your top competitors to understand what their customers love and hate. This can reveal market gaps you can fill or weaknesses you can exploit in your own positioning.
• Conduct Sentiment Analysis: By organizing dozens or hundreds of reviews, you can run a sentiment analysis to gauge the overall feeling about your brand. Tracking this over time can show you whether your customer satisfaction is trending up or down.

A Quick Word on Legality and Ethics

Before you start, it's important to understand the landscape. Scraping can be a legal and ethical gray area. While this isn't legal advice, here are a few key things to keep in mind:

• Facebook's Terms of Service: Facebook's policies generally prohibit automated data collection (scraping) from their site without their express written permission. Violating these terms could lead to your account being restricted or banned. Always proceed with caution.
• Public vs. Private Data: Only scrape data that's publicly available. In this case, Facebook business page reviews are public. Never attempt to scrape profiles, private messages, or content from private groups.
• Privacy and Personal Data: Be mindful of regulations like GDPR and CCPA. Avoid collecting and storing personally identifiable information beyond what's necessary for your analysis. The goal is to analyze sentiment and feedback, not compile profiles of individual users.

In short, use any scraped data responsibly and ethically. The primary goal is internal analysis for business improvement, not public redistribution of personally identifiable information.

Four Methods for Scraping Facebook Reviews

Depending on your technical comfort level and how many reviews you need, one of these four methods should work for you.

Method 1: The Manual Approach (Copy &, Paste)

This is the most straightforward, non-technical way to get the job done. It requires no special tools - just your web browser and a spreadsheet program like Google Sheets or Excel.

Best for: Businesses with a small number of reviews (under 50) or one-time data collection needs.

How to Do It:

1. Navigate to the Facebook page you want to scrape.
2. Find and click on the "Reviews" or "Recommendations" tab.
3. Scroll down to load as many reviews as you need. This is a critical step, as Facebook only loads a handful at a time. Keep scrolling until you've loaded all the reviews you wish to collect.
4. Carefully highlight the text of the reviews, including the reviewer's name, their recommendation (e.g., "recommends" or "does not recommend"), and the review text itself.
5. Copy the highlighted text (Ctrl+C or Cmd+C).
6. Paste it into a spreadsheet (Ctrl+V or Cmd+V).
7. Clean up the data. You'll have to manually separate the reviewer's name, the rating, and the text into different columns for it to be useful.

Pros: Totally free, no technical skills required, and carries the lowest risk of violating Facebook's terms of service since it's not automated.

Cons: Extremely time-consuming, prone to human error, and completely impractical for pages with hundreds or thousands of reviews.

Method 2: Using a Browser Extension Scraper

If the manual approach sounds like a nightmare, a browser extension can automate most of the copying and pasting for you. These tools work by letting you "point and click" at the data you want on a webpage, and they'll extract and organize it for you.

Best for: People who are comfortable installing and configuring browser addons and need to scrape a moderate number of reviews (50-200).

How to Do It (General Steps):

1. Find a reputable scraper extension on the Chrome Web Store or Firefox Add-ons store. Popular options often include "Web Scraper" or "Instant Data Scraper."
2. Install the extension in your browser of choice.
3. Navigate to the Facebook page's Reviews/Recommendations tab. As with the manual method, scroll down patiently to load all the reviews you want to capture.
4. Activate the extension by clicking its icon in your browser toolbar.
5. Define the data to scrape. The tool will ask you to select the elements on the page you want to extract. You'll typically click on one reviewer's name, one recommendation text, one review body, etc., and the extension's AI will attempt to find all similar elements on the page.
6. Run the scraper. Once you've defined what you're looking for, tell the tool to start scraping. It will go through the page and pull all the data into a table.
7. Export the results. Most extensions let you download your data as a CSV or Excel file, neatly organized into columns.

Pros: Much faster than manual copying, requires no coding, and organizes the data automatically into a structured format.

Cons: Has a learning curve, can sometimes fail if Facebook changes its page structure, and still requires you to manually scroll to load all the reviews.

Method 3: Writing a Custom Script (Python &, Selenium)

For those with programming skills, writing your own script offers the most power and flexibility. This approach involves using a language like Python along with libraries designed for web automation and scraping, such as Selenium (to control the browser) and BeautifulSoup (to parse the HTML).

Best for: Developers, data analysts, or technical marketers who need full control over the scraping process and plan to collect data regularly.

Conceptual Workflow:

A typical script would perform these actions automatically:

1. Launch a web browser (e.g., Chrome) controlled by the Selenium library.
2. Navigate to the specific Facebook page's review URL.
3. Handle the login and cookie popups if necessary.
4. Simulate scrolling down the page repeatedly to trigger the dynamic loading of more reviews. The script would need to wait for new content to load before scrolling again.
5. Once all reviews are loaded, the script would get the page's HTML source code.
6. Use a parsing library like BeautifulSoup to find all the HTML elements that contain the review data (e.g., divs with specific class names for the reviewer's name, the review content, etc.).
7. Extract the text from these elements and store it in a structured way (like a list of dictionaries).
8. Finally, write the structured data to a CSV file.

Pros: Fully automated, highly customizable, and can be scheduled to run on a regular basis. You have complete control over what data you get and how it's formatted.

Cons: Requires coding skills, is very brittle (it can break with any small change Facebook makes to its website design), and maintenance can be a hassle.

Method 4: Using a Third-Party SaaS Scraping Tool

If you need reliable, large-scale data collection without the maintenance headache of a custom script, several companies offer web scraping as a service. These professional-grade tools are designed to handle the complexities of scraping modern websites like Facebook.

Best for: Businesses, marketing agencies, or data teams that rely on this data for critical operations and need a reliable, scalable solution.

How They Work:

Services like Apify, Bright Data, or PhantomBuster provide "pre-built" scrapers or APIs specifically for platforms like Facebook. Instead of writing code, you configure a few settings:

1. Sign up for the service of your choice.
2. Find their pre-built "Facebook Page Reviews Scraper" or similar tool.
3. Enter the URL of the Facebook page(s) you want to scrape.
4. Configure specifications like how many reviews to fetch.
5. Run the tool. The service takes care of all the technical heavy lifting on its own servers - things like managing proxies to avoid getting blocked and keeping the scraper updated when Facebook changes its code.
6. Download your data in a clean, structured format (usually JSON, XML, or CSV).

Pros: The most reliable and hassle-free method. No coding required, maintained by a dedicated team, and built to handle scraping at scale.

Cons: This is a paid service, and costs can increase based on the volume of data you collect.

Final Thoughts

Harvesting your Facebook reviews provides an unfiltered look at what your customers truly think, giving you the raw material to refine your business. Whether you opt for a quick manual copy-paste, a handy browser extension, a powerful custom script, or a robust third-party service, the goal is the same: turn scattered feedback into structured, actionable insights.

Once you've analyzed what your audience is saying, the next step is managing the ongoing conversation and responding to that feedback directly. After scraping and identifying trends, actually engaging with every new comment and DM can feel like another full-time job. To solve that, we built Postbase with a unified inbox that brings all your communications from all your platforms into one streamlined view. It helps you stop switching between apps so you can focus on building your community around the awesome feedback you've just collected.