️ Grubhub Restaurant Reviews Scraper

Overview

Extracts detailed customer reviews and ratings from Grubhub restaurant pages. It collects structured data including review content, ratings, reviewer information, order details, menu items, and sentiment analysis. Built for reliability with proxy rotation and anti-blocking measures.

Key Features

• Scrapes comprehensive review data from specified restaurant URLs.
• Captures star ratings, review text, and timestamps.
• Includes reviewer details: name, diner type, and review count.
• Extracts order specifics: menu items, order accuracy, and food quality metrics.
• Provides automated sentiment analysis (Positive/Neutral/Negative).
• Handles rate limiting, retries, and proxy rotation to avoid blocks.

How to Use

Configure the actor with input parameters and run it. The results are saved to a dataset in your Apify storage, available for download in multiple formats (JSON, CSV, Excel, etc.).

Input Parameters

• restaurantUrls (required): An array of one or more Grubhub restaurant URLs to scrape.
• maxItems (optional): Limit the total number of reviews to collect.
• proxyConfiguration (optional): Use proxy settings if required.

Input Example

{
  "restaurantUrls": [
    "https://www.grubhub.com/restaurant/noodle-world-1935-s-atlantic-blvd-monterey-park/2249885/"
  ],
  "maxItems": 50
}

Output

The actor outputs a JSON array of review objects. Each object contains:

• restaurantUrl: The source URL.
• content: The review text.
• star_rating: Numerical rating.
• reviewer: Reviewer's name.
• diner_type: e.g., "Regular".
• review_count: Number of reviews by this user.
• review_created_date & order_placed_time: Timestamps.
• sentiment: Automated sentiment label.
• menu_items: Array of items ordered (with IDs and names).
• facet_responses: Includes order_accuracy and food_quality metrics.

Output Sample

[
  {
    "restaurantUrl": "https://www.grubhub.com/restaurant/noodle-world-1935-s-atlantic-blvd-monterey-park/2249885/",
    "content": "I love the variety and the food is always great",
    "reviewer": "Maria",
    "star_rating": 5,
    "diner_type": "Regular",
    "review_count": 2,
    "sentiment": "Positive",
    "menu_items": [
      { "id": "20522860476", "name": "THAI COFFEE" },
      { "id": "20522860413", "name": "21. Seafood Tom Yum." }
    ],
    "facet_responses": {
      "order_accuracy": "Yes",
      "food_quality": "Yes"
    }
  }
]

Use Cases

• Market research and competitor analysis.
• Tracking restaurant performance and customer satisfaction.
• Analyzing menu item popularity.
• Monitoring review sentiment trends over time.

Related Actors

• ️ Grubhub Restaurant Scraper - For restaurant data, menus, and delivery info.
• ️ Uber Eats Store Search Scraper
• ️ Zomato Restaurant Reviews Scraper
• ️ Myntra Reviews Scraper
• ️ Flipkart Review Scraper
• ️ Noon.com Reviews Scraper
• Hotels.com Review Scraper
• TripAdvisor Reviews Scraper