Yelp Business Scraper — Listings, Reviews, Ratings by Category and Location

Get every Yelp business in any city — with full review datasets — in one run. Search by keyword + location, filter by category / price / rating, and get clean JSON back: business profile, reviews (with sentiment-rich metadata), photos, hours, contact info.

No Yelp Fusion API signup. No 5,000 call/day ceiling. No deprecated review-scraping endpoint that Yelp shut down in 2018. This actor handles the live Yelp UI with rotating residential fingerprints and gives you the dataset the official API no longer returns.

Who uses this

• Local-SEO agencies tracking client reviews and competitor reviews across target cities.
• Franchise ops teams aggregating all review activity across 50-500 locations daily.
• Market researchers mapping the restaurant / salon / auto-shop competitive landscape in a city before client engagement.
• Lead-generation teams building B2B prospect lists (independent restaurants, salons, gyms) filtered by review count and rating.
• Data scientists training sentiment-classification models on a genuine local-business review corpus.

What you get per business

Field	Type	Example
business_id	string	"stripe-sf-coffee"
name	string	"Stripe SF Coffee"
rating	number	4.5
review_count	number	312
price	string	$$
categories	array	["Coffee & Tea", "Cafes"]
address	string	"510 Townsend St, San Francisco, CA 94103"
latitude	number	37.7749
longitude	number	-122.4194
phone	string	"+14155551234"
hours	object	{mon: "7:00-18:00", tue: ...}
website	string	https://example.com
is_closed	boolean	false
photo_urls	array	[...]
attributes	object	{wheelchair_accessible: true, accepts_credit_cards: true, outdoor_seating: true, ...}

What you get per review

Field	Example
review_id	"aB3cD..."
rating	5
text	"Favorite coffee spot near the office..."
author_name	"Jane D."
author_location	"San Francisco, CA"
author_review_count	47
author_is_elite	true
date	2026-03-12
useful_votes	12
funny_votes	2
cool_votes	5
photos	array of review-photo URLs

Input

{
  "searchTerm": "coffee",
  "location": "San Francisco, CA",
  "maxBusinesses": 50,
  "maxReviewsPerBusiness": 100,
  "priceFilter": ["$", "$$"],
  "openNow": false,
  "minRating": 4.0,
  "sortBy": "highest_rated"
}

• searchTerm — keyword. Leave empty to search by category only.
• location — city + state, neighborhood, or precise address.
• maxBusinesses — cap on businesses returned. Default 100.
• maxReviewsPerBusiness — cap on reviews per business. Set 0 to skip reviews.
• priceFilter — subset of $, $$, $$$, $$$$.
• minRating — minimum star rating filter.
• sortBy — highest_rated, most_reviewed, distance, recommended.

Python usage example

from apify_client import ApifyClient
import statistics

client = ApifyClient("YOUR_APIFY_TOKEN")

# Top-rated coffee shops in SF, with recent reviews
run_input = {
    "searchTerm": "coffee",
    "location": "San Francisco, CA",
    "maxBusinesses": 30,
    "maxReviewsPerBusiness": 50,
    "sortBy": "highest_rated",
}

run = client.actor("knotless_cadence/yelp-business-scraper").call(run_input=run_input)
items = list(client.dataset(run["defaultDatasetId"]).iterate_items())

# The dataset mixes business records and review records — split them
businesses = [x for x in items if x.get("type") == "business"]
reviews = [x for x in items if x.get("type") == "review"]

print(f"Found {len(businesses)} businesses, {len(reviews)} reviews")

# Top 5 by weighted sentiment (rating × log(review_count))
import math
ranked = sorted(
    businesses,
    key=lambda b: b["rating"] * math.log(max(b["review_count"], 2)),
    reverse=True,
)[:5]

for b in ranked:
    print(f"  {b['rating']} ★ ({b['review_count']:>4} reviews)  {b['name']}")

Common questions

Q: Is this more reliable than the Yelp Fusion API? For review data, yes — Yelp's official API returns only 3 "snippet" reviews per business since 2018. This actor returns all public reviews. For structured data (business profile, hours, categories), both are reliable; the Fusion API may be preferable if you have a key and don't need reviews.

Q: How fresh are the reviews? Live at run time. Running daily provides reliable new-review deltas.

Q: Can I filter by Elite reviewers only? Yes — filter the returned dataset on author_is_elite == true. Elite reviews typically carry more weight in Yelp's ranking algorithm.

Q: Does this work for Yelp.ca, Yelp.co.uk, etc.? Yes — pass the local URL or a location in the target country ("Toronto, ON", "London, UK"). Supported: US, CA, UK, IE, AU, NZ, FR, DE, IT, ES, NL, BE.

Q: What's the cost? Apify compute-unit pricing. A 50-business + 50-review run typically costs $0.03-$0.08.

Q: How do I avoid scraping the same business twice over multiple runs? business_id is stable across runs — dedupe on that field in your downstream pipeline.

Export integrations

• CSV / JSON / Excel / HTML (native Apify dataset download)
• Google Sheets (via Apify integration)
• Webhooks on each run
• S3 / GCS direct sync
• Zapier / Make.com / n8n

Related scrapers

Source	Actor	Data
Yelp (this)	Local-business reviews	Local sentiment
Google Maps	Business listings	Local data
Trustpilot	Service reviews	Brand reputation
Glassdoor	Employee reviews	Workforce sentiment
Reddit	Discussion threads	Unfiltered opinion

All free on Apify Store.

About the maintainer

Maintained by an active Apify developer (78 public actors covering scraping, MCP servers, local-business data, and review-mining tools). Technical articles on Hashnode, Telegraph, Write.as, and Mataroa covering scraping architecture, local-SEO data pipelines, and sentiment analysis.

Need a custom local-business intelligence pipeline (multi-city review tracking, competitor benchmarking, franchise rollups)? Email spinov001@gmail.com or message t.me/scraping_ai.

Disclaimer

Designed for market-research, local-SEO, and academic use. Respect Yelp's Terms of Service, applicable data-protection law (GDPR, CCPA), and scrape publicly visible content only. Not affiliated with Yelp Inc.