If you want a clear-eyed view of what people actually think about your brand, you need to go beyond your own website. Customers are talking everywhere—Google, Yelp, Trustpilot, obscure forums you’ve never heard of. Tracking all those reviews manually? That’s a never-ending chore. This guide is for folks who want a realistic, hands-on way to collect customer reviews from all over the web using Scrapingbee, so you can actually keep up with what matters.
Let’s get practical. Here’s how you can pull in customer reviews from multiple sources, avoid common headaches, and use what you find to stay ahead of problems before they blow up.
Why bother collecting reviews from multiple places?
- No one source tells the whole story—Google reviews might look great, but what if you’re getting roasted on Reddit?
- Spot problems early—You’ll catch patterns (good or bad) before they become public crises.
- Competitive edge—See how you stack up against others, and respond faster.
If you’re only watching one or two review sites, you’re basically flying blind.
Step 1: Pick your review sources (and don’t get greedy)
First, decide where you actually need to pull reviews from. Don’t try to scrape the entire internet—stick with the sites that matter for your brand.
Common choices: - Google Maps/Google Reviews - Yelp - Trustpilot - TripAdvisor - Amazon product reviews - Reddit (for mentions and informal feedback) - Niche forums or industry-specific sites
Pro tip:
Start with 2–3 sources. If you try to do everything at once, you’ll never get past setup.
What to ignore:
Sites with strong anti-bot protection or ones that require logins for every page (looking at you, Facebook) are often more hassle than they’re worth.
Step 2: Get access to Scrapingbee (and why it helps)
Scraping reviews isn’t just about fetching HTML—it’s dealing with JavaScript-heavy pages, bot detection, and weird layout changes. That’s where Scrapingbee comes in. It’s a web scraping API that handles headless browsers, proxies, and the stuff that gets you blocked.
Why use a service like Scrapingbee? - Handles JavaScript-rendered pages (most review sites use these) - Takes care of proxies and captchas (mostly) - Saves you from running and maintaining your own scraping servers - Simple API—no big frameworks to learn
What Scrapingbee won’t do: - Break through every anti-scraping measure (nothing’s foolproof) - Parse every site perfectly out of the box—you’ll still need to write some code
Set up: - Sign up for an account - Get your API key
That’s it. No hardware to provision, no browser automation headaches.
Step 3: Map out your targets and figure out the selectors
Before writing any code, open up the sites you want to scrape and inspect the reviews section. You need to know: - The URL patterns for pages you want to scrape (e.g., business pages, product pages) - The HTML structure—what tags or classes the reviews live in - How pagination works (do reviews load as you scroll? Next page buttons?)
Walkthrough example (Yelp):
1. Go to a Yelp business page.
2. Right-click a review, hit “Inspect.”
3. Find the container element for a review (often a <div> with a class like review__373c0__13kpL).
4. Note the data you want: reviewer name, date, rating, text.
Pro tip:
Sites change their HTML all the time. Expect your selectors to break every few months.
Step 4: Write your scraping script using Scrapingbee
Here’s the practical bit. You’ll need to write a script (Python is common, but use what you know) that:
- Loops through your list of URLs (businesses, products, etc.)
- For each, calls the Scrapingbee API to fetch the rendered HTML
- Parses out the reviews using your chosen selectors
Python example:
python import requests from bs4 import BeautifulSoup
API_KEY = 'YOUR_SCRAPINGBEE_API_KEY' TARGET_URL = 'https://www.yelp.com/biz/some-business'
def get_reviews(url): response = requests.get( 'https://app.scrapingbee.com/api/v1/', params={ 'api_key': API_KEY, 'url': url, 'render_js': 'true' } ) soup = BeautifulSoup(response.content, 'html.parser') # Update this selector as needed reviews = soup.find_all('div', class_='review__373c0__13kpL') for review in reviews: text = review.get_text() print(text)
get_reviews(TARGET_URL)
Things to watch out for: - You need to update selectors as sites change. - Pagination: To get more than the first page of reviews, you’ll need to loop through multiple URLs or simulate scrolling. - Rate limits: Don’t hammer the API or target sites. Scrapingbee has its own usage limits, and sites may block you if you go too fast.
Step 5: Handle pagination and dynamic loading
Most review sites don’t show all reviews at once. Some use “Next” buttons, others load content as you scroll.
How to deal with it: - For “Next” buttons, increment the page number in the URL and loop. - For infinite scroll, you might have to simulate scrolling (Scrapingbee can run custom JS, but this gets trickier). - Set a maximum number of pages or reviews to scrape per run—don’t try to grab everything in one go.
Example for paginated URLs:
python for page in range(0, 5): # First 5 pages (adjust as needed) paged_url = f'https://www.yelp.com/biz/some-business?start={page*20}' get_reviews(paged_url)
What doesn’t work well: - Sites with heavy anti-bot JavaScript (like Google Reviews) may block or deliver incomplete content. - If reviews are loaded only after certain user actions (like clicking tabs), you might be out of luck unless you get fancy with scripting.
Step 6: Parse and clean your data
Once you have the raw HTML, you need to extract the data in a way that’s actually useful.
What to capture: - Reviewer name or ID - Date of review - Star rating or score - Review text - (Optional) Any replies from your company
Cleaning tips: - Strip out HTML tags, emojis, and weird formatting - Convert dates to a standard format - Make sure ratings are numbers, not strings
Don’t obsess over edge cases at first. Get the basics working, then handle the weird stuff later.
Step 7: Store and analyze your reviews
Where you put this data depends on your needs: - CSV files: Fine for small projects or quick analysis in Excel - Databases (SQLite, Postgres): Better for ongoing monitoring or larger datasets - Google Sheets: Works for small teams, just don’t expect it to scale
Basic analysis ideas: - Track average rating over time - Flag new 1-star reviews for follow-up - Look for spikes in volume (good or bad) - Watch for recurring keywords (“slow,” “friendly,” “expensive”)
Don’t try to build a dashboard from day one. Start simple: get the data, look at it, then automate the parts you actually use.
Step 8: Keep it running (and don’t let it break quietly)
Set up your script to run on a schedule—daily or weekly is usually enough.
- Use cron jobs (Linux/Mac) or Task Scheduler (Windows)
- Log errors and send yourself alerts if something breaks (so you’re not flying blind)
- Periodically check if your selectors still work—sites will change, and your script will fail at some point
Pro tip:
If you suddenly get zero results, the site probably changed layout or blocked you. Don’t panic—just update your selectors and try again.
Things that don’t work (and why)
- Scraping sites with strong anti-bot measures (like Facebook, Google reviews): You’ll waste hours fighting captchas, headless browsers, and 2FA. If you must, look for official APIs or third-party aggregators.
- Trying to scrape “everything” in one run: You’ll hit rate limits or get blocked. Go slow and spread requests out.
- Relying on 100% automation: Sometimes you’ll need to fix things by hand. That’s just scraping life.
Wrapping up: Keep it simple and stay flexible
Brand monitoring with scraping isn’t rocket science, but it does take some trial and error. Start with one or two sources, get your scripts working, and check the results yourself before scaling up. Don’t try to be perfect—just get the reviews flowing, then build on what you learn.
And remember: the goal isn’t to automate everything forever. It’s to know what your customers are actually saying, so you can do something about it.