Olery - Worldwide Consumer Review Data | Hospitality, Travel & Tourism Data | Destination API | Sentiment Analysis
# | 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
|
Attribute | Type | Example | Mapping |
---|---|---|---|
Text | 10 Simpson Crescent | Company ID |
Description
Geography
History
Volume
2 | Billion Records |
Pricing
License | Starts at |
---|---|
One-off purchase | Not available |
Monthly License |
€25,000 / 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 - Worldwide Consumer Review Data Hospitality, Travel & Tourism Data Destination API Sentiment Analysis?
Consumer 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 - Worldwide Consumer Review Data Hospitality, Travel & Tourism Data Destination API Sentiment Analysis 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 - Worldwide Consumer Review Data Hospitality, Travel & Tourism Data Destination API Sentiment Analysis?
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 - Worldwide Consumer Review Data Hospitality, Travel & Tourism Data Destination API Sentiment Analysis go?
This Data API has 10 years of historical coverage. It can be delivered on a monthly basis.
Which countries does Olery - Worldwide Consumer Review Data Hospitality, Travel & Tourism Data Destination API Sentiment Analysis cover?
This product includes data covering 249 countries like USA, China, Japan, Germany, and India. Olery is headquartered in Netherlands.
How much does Olery - Worldwide Consumer Review Data Hospitality, Travel & Tourism Data Destination API Sentiment Analysis cost?
Pricing for Olery - Worldwide Consumer Review Data Hospitality, Travel & Tourism Data Destination API Sentiment Analysis starts at EUR25,000 per month. Connect with Olery to get a quote and arrange custom pricing models based on your data requirements.
How can I get Olery - Worldwide Consumer Review Data Hospitality, Travel & Tourism Data Destination API Sentiment Analysis?
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 - Worldwide Consumer Review Data Hospitality, Travel & Tourism Data Destination API Sentiment Analysis?
You can compare and assess the data quality of Olery using Datarade’s data marketplace.
What are similar products to Olery - Worldwide Consumer Review Data Hospitality, Travel & Tourism Data Destination API Sentiment Analysis?
This Data API has 3 related products. These alternatives include Olery - Oceania Tourist Attraction Data Hospitality, Travel & Tourism Data Destination API Sentiment Analysis Consumer Review Data, The Data Appeal Hospitality, Travel and Tourism Data API, Dataset 200 Million+ POI Data Mapped Coverage from 2019, and Grepsr Yelp Resturants Address and Reviews Data Global Coverage with Custom and On-demand Datasets. 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.