Yelp Review Scraper for Reviews and Business Data

A Yelp review scraper is useful when the target is more specific than "get me Yelp data." You may need review text for reputation analysis, business listings for local lead research, menus for restaurant intelligence, or detailed business records for enrichment. Those are different jobs, and they should not all start from the same request.

The Yelp Scraper in MintAPI separates those workflows into tabs: search, business details, reviews, and menus. That keeps the first run small. You can test one Yelp scrape, inspect the table and raw JSON, then decide whether the same workflow should become an API call or stay a dashboard export.

If you are deciding how to scrape Yelp data, pick the source that matches the question. Search is for discovery. Details are for enrichment. Reviews are for text and rating analysis. Menus and popular dishes are for restaurant-specific datasets.

• Use Yelp Search when you have a term and location, such as `pizza` in `Brooklyn, NY`.
• Use Business Details when you already have Yelp business URLs or business IDs and need richer profile objects.
• Use Reviews when the output should be review text, rating filters, sort order, and pagination state.
• Use Business URL to ID when your workflow starts with URLs but downstream calls need stable business IDs.
• Use Menus and Popular Dishes when restaurant menu or dish-level data is available for the business.

This workflow split matters for cost and output quality. A broad Yelp web scraper that tries to pull everything in one pass usually creates noisy rows. A staged workflow gives you a cleaner candidate list, then spends additional requests only on businesses worth enriching.

Review scraping starts from a known business, not a search term. The MintAPI Yelp Reviews endpoint accepts a Yelp business URL or a business ID. Optional controls include reviews_per_page, end_cursor, sort_by, and rating_filter.

In the dashboard, the Reviews tab wraps those controls into a form. Paste one or more business URLs or IDs, choose the sort order, choose the rating filter, set a bounded review count, or enable all reviews for the selected targets. The saved run keeps the original input, logs, table rows, raw JSON, and CSV output together.

1curl --request GET \2  --url 'https://api.mintapi.dev/api/yelp/reviews?business_id=BCUhfgjbVVvjs0ro4ATRsg&reviews_per_page=20&sort_by=Yelp_sort&rating_filter=All_ratings' \3  --header 'Authorization: Bearer YOUR_API_KEY'

Many local research tasks start with a category and location: dentists in Austin, coffee shops in Williamsburg, electricians in Phoenix, or restaurants near a venue. In that case, start with Yelp Search. The required inputs are location and search_term. The documented controls include limit, offset, and business_details_type.

Search rows can include business IDs, names, aliases, address fields, review counts, average ratings, price signals, categories, and photo URLs. That gives you enough structure to filter and deduplicate before calling richer endpoints. For broad local coverage strategy, compare this with Google Maps place data parsing, where the same search-plan principle applies to local business discovery.

After search, use enrichment only where it adds value. The Yelp Business Details endpoint accepts a full Yelp business URL or comma-separated business IDs. It is the better follow-up when you need a detailed profile payload rather than a search-result row.

If your input list came from pasted URLs, the Business URL to ID endpoint can resolve a profile URL into a business ID, alias, locality, coordinates, and rating summary. That makes multi-step workflows less fragile because later review, menu, or popular-dish calls can use the same business identifier.

Yelp data is not only useful for review text. Restaurant workflows often need menus, dish names, prices, photos, or dish-level review counts. MintAPI exposes those through Get Menus and Popular Dishes where Yelp returns the data.

The dashboard menu workflow lets you enable menus, popular dishes, or both for the same set of business URLs or IDs. That is useful when the analyst is still checking coverage. Once the output shape is validated, the same calls can move into a scheduled backend or an n8n workflow. For that kind of orchestration, see MintAPI for n8n automation.

There is no special Python-only path. A Yelp scraper Python workflow can call the same HTTPS endpoints that the dashboard uses. Keep the request explicit, store the response JSON, and paginate only after you have confirmed that the first page contains the data you need.

1import requests23url = "https://api.mintapi.dev/api/yelp/search"4params = {5    "location": "Brooklyn, NY",6    "search_term": "pizza",7    "limit": 10,8    "offset": 0,9    "business_details_type": "basic",10}11headers = {"Authorization": "Bearer YOUR_API_KEY"}1213response = requests.get(url, params=params, headers=headers, timeout=30)14response.raise_for_status()15data = response.json()1617for business in data.get("business_search_result", []):18    print(business.get("id"), business.get("name"), business.get("avg_rating"))

For agentic calls, MintAPI also supports the documented paid request flow. The paidFetch and paidJson docs explain how a runtime can handle 402 Payment Required, sign an accepted payment option, and retry the request.

Use the dashboard when a person is still deciding the right Yelp scrape: which location, which search term, which business IDs, which review sort order, and which export shape. Use direct API calls when the workflow is scheduled, embedded in a product, or controlled by an agent or automation tool.

The broader Yelp API page lists the endpoint family, while the dashboard Yelp scraper console is the fastest way to test inputs and export a one-off CSV. If you are designing broader social or local data systems, the patterns in API patterns for social media data are relevant even though Yelp is a local business source rather than a social feed.

• Use search for discovery, details for enrichment, reviews for review text, and menus or dishes for restaurant-specific data.
• Run a small dashboard scrape before automating a larger Yelp web scraper workflow.
• Use business IDs where possible so review, menu, and popular-dish calls stay stable.
• Keep review jobs bounded unless you have already validated the target and budget.
• Move validated workflows into API calls when the job needs scheduling, agents, or product integration.