Yelp Reviews Scraper for Business Feedback

Turn public Yelp business URLs or slugs into a source-linked review dataset for reputation teams, local-market researchers, analysts, and AI agents. Each review row can include the rating, full text, publication time, author details, reactions, photos, owner reply, review URL, business context, and collection time. An optional business summary row keeps review analysis connected to the location it describes.

Best fit for this Actor

• Choose this Actor when the starting point is a public Yelp business URL or business slug and the goal is review-level feedback.
• Use newest, oldest, rating, relevance, language, and date-window controls to shape the review sample around the research question.
• For business discovery by keyword and location, start with the Yelp Business Data Scraper, then pass the returned public Yelp business URLs into this Actor.

From a business page to a feedback brief

A reputation analyst starts with the Yelp URLs for selected locations and requests recent reviews in date order. The Actor returns one review row at a time with text, rating, author, owner response, reactions, and source URLs. The analyst then groups recurring topics, compares location-level feedback, and keeps the original review and business URLs beside every finding for review.

Quick start input

{
  "startUrls": [
    { "url": "https://www.yelp.com/biz/molinari-delicatessen-san-francisco" }
  ],
  "maxReviewsPerBusiness": 50,
  "sortBy": "date_desc",
  "language": "en",
  "includeBusinessSummary": true
}

You can also provide bare values through businessSlugs. When both inputs are present, the Actor combines and deduplicates them before collection.

Input reference

Field	Type	What it controls
startUrls	array	Public Yelp business pages supplied as request-list sources.
businessSlugs	array	Bare Yelp business slugs for spreadsheet and database workflows.
maxReviewsPerBusiness	integer	Stable per-business cap that bounds dataset size and event charges.
sortBy	string	Yelp review ordering by recency, rating, relevance, or Elite status.
language	string	Review language selection, including an all-languages mode.
dateFrom, dateTo	string	Inclusive date window in YYYY-MM-DD format.
includeBusinessSummary	boolean	Adds one business-context row alongside its review rows.
proxyConfiguration	object	Apify proxy settings for the collection route.

What data you receive

The default dataset contains review rows and, when selected, business_summary rows. recordType makes the two shapes easy to route in a database or agent workflow.

Field group	Useful fields
Review identity	recordType, reviewId, reviewUrl, businessSlug
Feedback	rating, text, language, createdAt, tags
Author	authorName, authorLocation, authorIsElite, authorReviewCount
Engagement	usefulCount, funnyCount, coolCount, photoUrls
Response	ownerReply, ownerReplyAt
Business context	businessName, businessUrl, businessAvgRating, businessAddress, businessCategories
Provenance	businessUrl, reviewUrl, scrapedAt

{
  "recordType": "review",
  "reviewId": "example-review-id",
  "businessSlug": "molinari-delicatessen-san-francisco",
  "businessName": "Molinari Delicatessen",
  "businessUrl": "https://www.yelp.com/biz/molinari-delicatessen-san-francisco",
  "rating": 5,
  "text": "Fresh ingredients and friendly service.",
  "language": "en",
  "createdAt": "2026-06-01T12:00:00.000Z",
  "authorName": "Example Reviewer",
  "usefulCount": 2,
  "ownerReply": "Thank you for visiting.",
  "reviewUrl": "https://www.yelp.com/biz/molinari-delicatessen-san-francisco?hrid=example-review-id",
  "scrapedAt": "2026-06-02T09:00:00.000Z"
}

Use through the API

Keep the Apify token in the authorization header and send the Actor input as JSON:

curl -X POST "https://api.apify.com/v2/acts/khadinakbar~yelp-reviews-scraper/runs" \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "startUrls": [{"url": "https://www.yelp.com/biz/molinari-delicatessen-san-francisco"}],
    "maxReviewsPerBusiness": 50,
    "sortBy": "date_desc"
  }'

After completion, read the default dataset for review rows and the OUTPUT record in the default key-value store for the terminal outcome and collection summary.

Use with AI agents through Apify MCP

Collect recent public reviews for these Yelp business URLs. Return one review per row with rating, text, publication time, author, owner reply, business URL, review URL, and collection time. Keep the scope bounded per business, read the dataset after completion, and summarize recurring feedback with source URLs beside each finding.

For dependable agent routing, provide canonical business URLs or slugs, request only the fields needed for the outcome, inspect the terminal outcome, and preserve dataset provenance. A bounded maxReviewsPerBusiness keeps scope and cost easy to review.

Connect the workflow

Start with the Yelp Business Data Scraper when the workflow begins with a category, keyword, or location. Continue with this Actor after discovery by feeding the selected public Yelp business URLs into startUrls for review-level analysis.

Pricing

This Actor uses Pay per event plus Apify platform usage. Open the live Pricing tab for current event details, and use maxReviewsPerBusiness with Apify run cost controls to keep the workflow aligned with your budget.

Best results

• Provide canonical public Yelp business pages or clean business slugs.
• Select date_desc for monitoring workflows and a rating sort for focused feedback research.
• Keep includeBusinessSummary enabled when downstream joins benefit from business name, address, categories, and aggregate rating.
• Preserve businessUrl, reviewUrl, and scrapedAt when comparing observations over time.

Builder's note

I built the dataset around review-level source links plus an optional business summary because I found that feedback becomes much easier to audit and join when the review, location, and collection time travel together. I also designed a second structured-page extraction path so useful public review context can still be captured when the primary review stream yields a smaller sample.

Responsible use

Collect public data you are authorized to access, preserve source context, and handle reviewer information in line with applicable laws, Yelp terms, and your organization's data policies.