Manually copying reviews off Yelp doesn’t scale past a handful of businesses. And the best Yelp scrapers should let you extract data with the least technical effort. ScrapeHero Cloud’s Yelp scrapers pull reviews, ratings, and business details into structured files you can plug straight into a sentiment model, a competitor dashboard, or a lead list.
- What’s the difference between ScrapeHero’s two Yelp scrapers, and which one fits your project?
- What fields does each scraper extract?
- How does the scraping workflow work, start to finish?
- What output formats, storage options, and scheduling are available?
- How does pricing and credit usage actually work?
- What are the common use cases and limitations to plan around?
ScrapeHero offers two purpose-built Yelp scrapers, and picking the right one depends on what you’re trying to build:
- Yelp Reviews Scraper: pulls the review text, ratings, and reviewer data for a given business. Use this for sentiment analysis, QA monitoring, and reputation tracking.
- Yelp Business Details Scraper: pulls the business profile itself (address, category, hours, aggregate rating, contact info) without pulling every individual review. Use this for lead generation, market mapping, and competitor benchmarking where you need business-level data, not review-level data.
The two run as separate crawlers, so you can use either on its own or combine them when a project needs both the business record and its review history.
What the Yelp Reviews Scraper Extracts
- Author and Author URL: the reviewer’s display name and profile link
- Review Body: the full text of the review
- Review Rating: the star score, 1 to 5
- Date Created: when the review was posted
- Images: URLs of any photos attached to the review
- Funny / Useful / Cool: Yelp’s engagement signals for each review
- Name, Address, Listing URL: basic identity and a link back to the Yelp listing
- Aggregate Rating and Review Count: the business’s overall score and review volume
What the Yelp Business Details Scraper Extracts
- Business Name and Yelp URL: the listing name and a link back to the Yelp page
- Address and Coordinates: full street address plus latitude and longitude
- Phone Number: the listed contact number
- Category: how Yelp classifies the business (e.g., “Italian,” “Auto Repair”)
- Price Range: Yelp’s dollar-sign rating, where available
- Hours of Operation: the listed weekly schedule
- Website: the business’s linked site, if listed
- Claimed Status: whether the owner has claimed the listing
- Aggregate Rating and Review Count: the business’s overall score and total review volume
- Photos: URLs of images attached to the listing
Because this scraper skips individual review records, it uses fewer credits per business than the Reviews Scraper, which makes it the better fit for jobs that only need business-level data, not the review text.
How the Scrapers Work
- Go to app.scrapehero.com and log in.
- Find the Yelp Reviews Scraper or Yelp Business Details Scraper in the App Store, depending on which data you need.
- Click “Create New Project.”
- Enter the Yelp business URLs you want to crawl.
- Set how many records you want returned and click “Gather Data.”
ScrapeHero handles pagination, page rendering, and data extraction on its own infrastructure, including its proxy network, so you’re not managing IPs or dealing with blocks yourself. That matters most on larger jobs: many businesses at once, or deep pagination through years of review history.
Note on location: ScrapeHero defaults to US-based locations for consistency. If you need geo-specific results from another region, contact support to set that up before running a large job.
Output Formats and Storage
Once a run finishes, download results as:
- Excel (.xlsx): for quick inspection and pivot tables
- CSV: for BI tools, Python or R workflows, and databases
- JSON: for API-style integrations and nested data structures
You can also connect cloud storage, such as Dropbox, so new runs save automatically to a designated folder. That gives you a versioned archive of review snapshots without manual file handling.
Scheduling and Automation
Both scrapers support scheduled runs, so your data refreshes on a cadence that matches how fast the situation moves:
- Hourly: high-velocity monitoring, such as during a PR issue or a product launch
- Weekly: the common choice for tracking sentiment trends and response SLAs
- Monthly: longitudinal studies and periodic reporting
Scheduled jobs keep a dashboard, alert system, or ML model fed with current data without anyone re-running the crawler by hand.
Pricing Model and Quotas
ScrapeHero runs on a monthly subscription, and usage is metered in page credits, not in the number of records you get back. That distinction is what determines your actual cost: the “number of records” field you set during setup controls how much data comes back, but how many credits that consumes depends on how many pages the crawler has to load to get there. A review-heavy business with deep pagination costs more credits than a business with a short review history, even if you request the same record count for both.
A few policy points to plan around:
- Credits reset each billing period and don’t roll over. Unused credits aren’t refunded.
- Misconfigured runs, such as wrong URLs, aren’t refunded either. Test with a small batch before committing to a full run.
- Canceling stops future billing after the current month, but any credits you have left can still be used until they run out.
Organizations with custom fields, geo-specific pricing capture, or high-volume needs can talk to ScrapeHero’s sales team about a custom plan.
Typical Use Cases
- Customer sentiment analysis: aggregate review text and ratings over time to catch emerging complaints or praise before they show up in your NPS score
- Competitive benchmarking: compare your rating distribution and review velocity against nearby competitors, using the Business Details Scraper to map the competitive set and the Reviews Scraper to go deep on the ones that matter
- Quality assurance: track specific keywords, like “cleanliness,” “noise,” or “Wi-Fi,” to prioritize operational fixes
- Market research: analyze language patterns across categories to see what customers actually value in a segment
- Lead generation and enrichment: use the Business Details Scraper to build datasets of local businesses with contact info and social proof metrics, without pulling every review
Limitations to Plan Around
- Pricing and availability on Yelp can be location-dependent; ScrapeHero’s US default may not fit projects centered outside the US without a support request.
- Yelp’s terms of service and anti-scraping measures mean large-scale or aggressive crawling should stay within reasonable limits.
- Budget for your credit usage based on pages, not records. Estimate how many review pages you’ll actually need per cycle, especially if you’re tracking many businesses or pulling long review histories.
Ready to pull structured Yelp data without the manual work? Start a free run on ScrapeHero Cloud or talk to sales about a custom plan for high-volume projects.
FAQ
Do I need two separate scrapers, or can one tool do both jobs?
They’re separate crawlers. Use the Business Details Scraper when you only need the business profile, and the Reviews Scraper when you need the review text and ratings. Run both if a project needs the full picture.
How is pricing calculated?
By page credits consumed, not by the number of records returned. Deep review histories or heavy pagination use more credits even at the same record count.
Can I schedule recurring pulls instead of running the scraper manually each time?
Yes. Both scrapers support hourly, weekly, and monthly scheduling.
