Olery - South America & Caribbean Hospitality Data | Hospitality, Travel & Tourism Data | Destination API | Sentiment Analysis | Restaurant Data
# | country |
region |
city |
topic |
ratings/review_count |
ratings/min |
ratings/max |
ratings/value |
sentiment/overall/review_count |
sentiment/overall/opinions_count |
sentiment/overall/sentiment_score |
sentiment/overall/positive_opinions |
sentiment/overall/negative_opinions |
sentiment/service/review_count |
sentiment/service/opinions_count |
sentiment/service/sentiment_score |
sentiment/service/positive_opinions |
sentiment/service/negative_opinions |
sentiment/cleanliness/review_count |
sentiment/cleanliness/opinions_count |
sentiment/cleanliness/sentiment_score |
sentiment/cleanliness/positive_opinions |
sentiment/cleanliness/negative_opinions |
sentiment/facilities/review_count |
sentiment/facilities/opinions_count |
sentiment/facilities/sentiment_score |
sentiment/facilities/positive_opinions |
sentiment/facilities/negative_opinions |
sentiment/location/review_count |
sentiment/location/opinions_count |
sentiment/location/sentiment_score |
sentiment/location/positive_opinions |
sentiment/location/negative_opinions |
sentiment/value/review_count |
sentiment/value/opinions_count |
sentiment/value/sentiment_score |
sentiment/value/positive_opinions |
sentiment/value/negative_opinions |
sentiment/fnb/review_count |
sentiment/fnb/opinions_count |
sentiment/fnb/sentiment_score |
sentiment/fnb/positive_opinions |
sentiment/fnb/negative_opinions |
sentiment/ambience/review_count |
sentiment/ambience/opinions_count |
sentiment/ambience/sentiment_score |
sentiment/ambience/positive_opinions |
sentiment/ambience/negative_opinions |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | xxxxxxxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxx | xxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxx | xxxxxx | Xxxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxx | xxxxxx | xxxxxxx | xxxxxxx | Xxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | xxxxxx | Xxxxxxxx |
2 | Xxxxxxx | Xxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxxxx | xxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxx | xxxxxxx | xxxxxxxxx | Xxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxx | xxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxx | Xxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxx | xxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxx | xxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxxx |
3 | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | xxxxx | xxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxxx | xxxxxxxxxx | Xxxxxx | xxxxxxxxx | Xxxxx | xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxx | xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx |
4 | xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | Xxxxx | Xxxxxx | Xxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx | xxxxx | Xxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxx | Xxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | Xxxxxx | xxxxxx |
5 | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxxx | xxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | Xxxxx | xxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxxxxx | xxxxxxx |
6 | Xxxxx | xxxxxxxxx | xxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx | Xxxxx | Xxxxx | Xxxxxxx | xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxx | xxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxx | Xxxxxxx | xxxxxxxx | xxxxx |
7 | Xxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxxxx | xxxxxxx | Xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxx | xxxxxx | xxxxxxxx | xxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxxx | xxxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxxx | Xxxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxxxxxx | xxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxx |
8 | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxx | xxxxxxxx | xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxx | xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxx | xxxxxx | Xxxxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxx | Xxxxx | Xxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxx | xxxxx | Xxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxx | xxxxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxxxxxxx | xxxxx | xxxxxxx | xxxxxxxxx | xxxxxxxxx | xxxxxx | xxxxxx |
9 | Xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxxx | xxxxx | xxxxx | Xxxxxxxxx | xxxxx | Xxxxx | Xxxxxx | xxxxxxxxxx | Xxxxxxxx | Xxxxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxxxx | xxxxxx | Xxxxx | Xxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxx | xxxxxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxxxxx |
10 | xxxxxx | xxxxxxxxxx | xxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxx | Xxxxxxxxxx | xxxxx | Xxxxxxxxx | Xxxxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxxxx | xxxxx | xxxxxxxxxx | xxxxxxx | xxxxxxxx | Xxxxx | xxxxx | xxxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxx | xxxxxx | xxxxxx | Xxxxxx | xxxxxx | xxxxx | Xxxxxxxxx | Xxxxxxxxx | Xxxxxx | Xxxxx | Xxxxxxxxx | Xxxxxx | Xxxxxx | xxxxxx | xxxxx | xxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxx |
... | Xxxxx | xxxxxxxxxx | Xxxxx | xxxxxxx | xxxxxxx | Xxxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxxxx | Xxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxxx | xxxxx | xxxxxx | xxxxxx | Xxxxxxxx | xxxxxxx | Xxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxx | Xxxxxx | xxxxxx | Xxxxxxxx | Xxxxx | xxxxx | Xxxxxxxxxx | xxxxxx | xxxxx | xxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxxx | xxxxxxxxxx | xxxxxxx | xxxxxxx | xxxxxxxxxx | Xxxxxxxxxx | Xxxxxx |
Data Dictionary
Attribute | Type | Example | Mapping |
---|---|---|---|
country
|
String | United States | |
region
|
String | New%20York%20(NY) | |
city
|
String | New York City | |
topic
|
String | overall | |
ratings/review_count
|
Integer | 118832 | |
ratings/min
|
Integer | 0 | |
ratings/max
|
Integer | 100 | |
ratings/value
|
Float | 87.77463982765585 | |
sentiment/overall/review_count
|
Integer | 52000 | |
sentiment/overall/opinions_count
|
Integer | 238461 | |
sentiment/overall/sentiment_score
|
Float | 8.804032371871568 | |
sentiment/overall/positive_opinions
|
Integer | 162981 | |
sentiment/overall/negative_opinions
|
Integer | 38984 | |
sentiment/service/review_count
|
|||
sentiment/service/opinions_count
|
|||
sentiment/service/sentiment_score
|
|||
sentiment/service/positive_opinions
|
|||
sentiment/service/negative_opinions
|
|||
sentiment/cleanliness/review_count
|
|||
sentiment/cleanliness/opinions_count
|
|||
sentiment/cleanliness/sentiment_score
|
|||
sentiment/cleanliness/positive_opinions
|
|||
sentiment/cleanliness/negative_opinions
|
|||
sentiment/facilities/review_count
|
|||
sentiment/facilities/opinions_count
|
|||
sentiment/facilities/sentiment_score
|
|||
sentiment/facilities/positive_opinions
|
|||
sentiment/facilities/negative_opinions
|
|||
sentiment/location/review_count
|
|||
sentiment/location/opinions_count
|
|||
sentiment/location/sentiment_score
|
|||
sentiment/location/positive_opinions
|
|||
sentiment/location/negative_opinions
|
|||
sentiment/value/review_count
|
|||
sentiment/value/opinions_count
|
|||
sentiment/value/sentiment_score
|
|||
sentiment/value/positive_opinions
|
|||
sentiment/value/negative_opinions
|
|||
sentiment/fnb/review_count
|
|||
sentiment/fnb/opinions_count
|
|||
sentiment/fnb/sentiment_score
|
|||
sentiment/fnb/positive_opinions
|
|||
sentiment/fnb/negative_opinions
|
|||
sentiment/ambience/review_count
|
|||
sentiment/ambience/opinions_count
|
|||
sentiment/ambience/sentiment_score
|
|||
sentiment/ambience/positive_opinions
|
|||
sentiment/ambience/negative_opinions
|
Description
Geography
History
Volume
2 | Billion + Records |
Pricing
License | Starts at |
---|---|
One-off purchase | Not available |
Monthly License |
€5,000 / month |
Yearly License | Not available |
Usage-based | Not available |
Suitable Company Sizes
Delivery
Use Cases
Categories
Related Searches
Related Products
Frequently asked questions
What is Olery - South America & Caribbean Hospitality Data Hospitality, Travel & Tourism Data Destination API Sentiment Analysis Restaurant Data?
Review Data on Attractions, Restaurants and Accommodations worldwide. Data Points - Numerical Ratings, Sentiment Ratings, Traveller Experience Score (TES), Travel Composition, Reviewer Origin (Nationality), Language, Group, Region, Country, City, Accommodation, Restaurants, Attractions,
What is Olery - South America & Caribbean Hospitality Data Hospitality, Travel & Tourism Data Destination API Sentiment Analysis Restaurant Data used for?
This product has 5 key use cases. Olery recommends using the data for Marketing, Competitor Insights, Trend Analysis, Know Your Customer (KYC), and Review Monitoring. Global businesses and organizations buy Consumer Review Data from Olery to fuel their analytics and enrichment.
Who can use Olery - South America & Caribbean Hospitality Data Hospitality, Travel & Tourism Data Destination API Sentiment Analysis Restaurant Data?
This product is best suited if you’re a Small Business, Medium-sized Business, or Enterprise looking for Consumer Review Data. Get in touch with Olery to see what their data can do for your business and find out which integrations they provide.
How far back does the data in Olery - South America & Caribbean Hospitality Data Hospitality, Travel & Tourism Data Destination API Sentiment Analysis Restaurant Data go?
This Data API has 10 years of historical coverage. It can be delivered on a monthly basis.
Which countries does Olery - South America & Caribbean Hospitality Data Hospitality, Travel & Tourism Data Destination API Sentiment Analysis Restaurant Data cover?
This product includes data covering 42 countries like Brazil, Argentina, Colombia, Chile, and Peru. Olery is headquartered in Netherlands.
How much does Olery - South America & Caribbean Hospitality Data Hospitality, Travel & Tourism Data Destination API Sentiment Analysis Restaurant Data cost?
Pricing for Olery - South America & Caribbean Hospitality Data Hospitality, Travel & Tourism Data Destination API Sentiment Analysis Restaurant Data starts at EUR5,000 per month. Connect with Olery to get a quote and arrange custom pricing models based on your data requirements.
How can I get Olery - South America & Caribbean Hospitality Data Hospitality, Travel & Tourism Data Destination API Sentiment Analysis Restaurant Data?
Businesses can buy Consumer Review Data from Olery and get the data via REST API. Depending on your data requirements and subscription budget, Olery can deliver this product in .json format.
What is the data quality of Olery - South America & Caribbean Hospitality Data Hospitality, Travel & Tourism Data Destination API Sentiment Analysis Restaurant Data?
You can compare and assess the data quality of Olery using Datarade’s data marketplace.
What are similar products to Olery - South America & Caribbean Hospitality Data Hospitality, Travel & Tourism Data Destination API Sentiment Analysis Restaurant Data?
This Data API has 3 related products. These alternatives include Olery - Worldwide Consumer Review Data Hospitality, Travel & Tourism Data Destination API Sentiment Analysis, Grepsr Yelp Resturants Address and Reviews Data Global Coverage with Custom and On-demand Datasets, and The Data Appeal Hospitality, Travel and Tourism Data API, Dataset 200 Million+ POI Data Mapped Coverage from 2019. You can compare the best Consumer Review Data providers and products via Datarade’s data marketplace and get the right data for your use case.