Best Restaurant Review Dataset
Restaurant review datasets are comprehensive collections of data that provide insights into customer reviews and ratings of various restaurants. These datasets encompass a wide range of information, including customer feedback, sentiment analysis, restaurant details, and user preferences. Leveraging these datasets, businesses can analyze customer sentiments, identify popular dishes, improve service quality, and make data-driven decisions to enhance the overall dining experience.
22 results
Coresignal | Company Data | Employee Review Data | Glassdoor + Indeed | Global / 33M+ Records / Updated Monthly
Language Name
Location Name
Product Rating
Available in
and 244 more countries
Consumer Review Data & Ratings, Business Listings Data from Yelp | Real-Time API
Latitude
Longitude
Address
Company Name
Language Name
and 8 more attributes
Available in
and 245 more countries
Grepsr| Yelp Resturants Address and Reviews Data | Global Coverage with Custom and On-demand Datasets
Company Name
Location Name
Product Rating
Location ID
Available in
and 244 more countries
Autoscraping | USA Restaurant Data | 500K+ Listings with Addresses, Ratings & Delivery Fees
Available in
Consumer Review Data - Trustpilot (EU and USA)
Brand Name
Job Source URL
Available in
and 17 more countries
Related searches
AI Training Data | Annotated Checkout Flows for Retail, Restaurant, and Marketplace Websites
Available in
DoorDash Restaurants List (all 294k records) | scrapelabs.io
City Name
Website
Phone Number
Available in
Worldwide enhanced F&B (restaurants, cafés, etc.) dataset, including metadata, social profiles, menus, reviews, pictures, and more.
Latitude
Brand Name
City Name
Longitude
Website
and 4 more attributes
Available in
and 19 more countries
Dataplex: Google Reviews & Ratings Dataset | Track Consumer Sentiment & Location-Based Insights
Available in
and 245 more countries
Transact Restaurants Transaction Data | USA Data | 100M Credit & Debit Cards, 12K Merchants, 800 Parent Companies, 600 Tickers
Available in