Skip to main content
Yelp is a strong source for local business and review intelligence. It is especially useful for restaurants, home services, health providers, beauty services, nightlife, and other categories where reviews influence buying decisions. Yelp scraping usually has two goals: collect business listings for a category and location, or collect reviews and profile details for known businesses. Keep those workflows separate.

​
Page types

Apify Yelp actors and Bright Data’s Yelp-focused tooling reflect this split: business data and reviews are often separate extraction targets. That keeps lead generation workflows lighter and review-analysis workflows more complete.

​
Search workflow

Start with a category and location:
Collect:
  • Business name
  • Yelp business URL
  • Category
  • Rating
  • Review count
  • Price level
  • Address
  • Neighborhood
  • Phone number
  • Website when visible
  • Search keyword and location
  • Result position
The search keyword and result position are important. A restaurant ranking first for brunch near me and tenth for coffee shop carries different market meaning.

​
Profile workflow

Business profiles add richer detail:
  • Opening hours
  • Photos
  • Amenities and service options
  • Delivery/takeout/reservation signals for restaurants
  • Payment, parking, accessibility, or Wi-Fi attributes when visible
  • Owner or business description
  • Map coordinates or neighborhood
  • Links to website or menu
Not every category has the same fields. A restaurant page and a contractor page expose different attributes. Build schemas that allow missing fields.

​
Review workflow

Yelp reviews are useful for sentiment analysis, competitor research, and service-quality monitoring. Collect:
  • Review rating
  • Review text
  • Review date
  • Reviewer display name or public profile URL
  • Reviewer location when visible
  • Photos
  • Owner response
  • Useful/funny/cool counts when visible
Yelp’s review ecosystem has its own filtering and ranking behavior. Do not assume the visible set is a complete or neutral sample of every customer experience. For analysis, record sort mode, collection date, and the review page URL.

​
Use cases

​
Local lead generation

Collect businesses in a city and category, then enrich websites for public emails or contact forms. Score leads by review count, rating, website quality, and category fit.

​
Competitor monitoring

Track competing businesses over time: rating changes, new reviews, review sentiment, hours changes, and category positioning.

​
Market research

Compare business density, review volume, and average ratings across neighborhoods or cities.

​
Review analysis

Extract review text for topic modeling: wait times, service quality, pricing complaints, staff mentions, food quality, or recurring product issues.

​
Technical considerations

Yelp pages can involve:
  • Pagination
  • Dynamic content
  • Category-specific layouts
  • Review sorting and filtering
  • Anti-bot checks
  • Missing fields by business type or region
Start with a small location/category combination and validate the output before scaling to many cities. For multi-city jobs, split by category and city so failures are easier to retry.

​
Compliance boundaries

Scrape only public data you are allowed to use, respect site terms and robots.txt, avoid collecting unnecessary personal data, and be careful when storing reviewer information. Yelp also provides official business APIs for some use cases; use official routes when they meet your needs.

​
Template vs custom workflow

Templates are helpful for common jobs: Yelp business listings, business profile details, and reviews. They handle pagination, browser rendering, field mapping, and exports. Custom workflows make sense when you need a specific category taxonomy, review sentiment pipeline, CRM enrichment, or city-by-city monitoring. For most Yelp projects, the best architecture is simple: scrape search pages to discover businesses, scrape profiles for full business context, scrape reviews only when sentiment or reputation analysis requires it, and keep source timestamps so changes can be tracked over time.