Web Scraping Data: Consumer/ Employee Sentiment Analysis | Glassdoor and G2 Review Web Data
# | company_id |
company_url |
headquarters |
company_founded |
average_rating |
percent_recommend |
percent_ceo_approval |
ceo_rating |
ceo_ratings_count |
business_outlook_rating |
compensation_and_benefits_rating |
culture_and_values_rating |
career_opportunities_rating |
recommend_to_friend_rating |
senior_management_rating |
work_life_balance_rating |
diversity_and_inclusion_rating |
diversity_and_inclusion_rating_count |
job_count |
salary_count |
review_count |
interview_count |
benefit_count |
interview_experience_counts_positive |
interview_experience_counts_negative |
interview_experience_counts_neutral |
record_date |
||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 |
2 | Xxxxx | xxxxxx | xxxxxxx | xxxxxxx | Xxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | xxxxxx | Xxxxxxxx | Xxxxxxx | Xxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxxxx | xxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxx | xxxxxxx | xxxxxxxxx | Xxxxxx | Xxxxxxx |
3 | 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 | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | xxxxx |
4 | 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 |
5 | Xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | 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 |
6 | xxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | Xxxxxx | xxxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxxx | xxxxxx | xxxxx | Xxxxxxxxxx |
7 | 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 | Xxxxx | xxxxxxxxx | xxxxxxxx | Xxxxxxxx | xxxxxxxx |
8 | 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 |
9 | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxx | Xxxxxxx | xxxxxxxx | xxxxx | 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 |
10 | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxxx | xxxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxxx | Xxxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxxxxxx | xxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxx | 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 | Xxxxxxxxxx |
Data Dictionary
Attribute | Type | Example | Mapping |
---|---|---|---|
company_id
|
String | 1,133,521 | |
company_url
|
String | https://www.glassdoor.com/Overview/Working-at-MAPFRE-ASIS... | |
String | Mapfre | Company Name | |
String | www.mapfre.com | Company Website | |
headquarters
|
String | Majadahonda, Spain | |
String | 10000+ Employees | Company Employee Count | |
String | Company - Public (MAP) | Organization Type | |
company_founded
|
Integer | 1935 | |
String | $10+ billion (USD) | Company Annual Revenue | |
String | To whom do you turn when your vehicle intersects with the... | Company Description | |
average_rating
|
Float | 3.8 | |
percent_recommend
|
Integer | 75 | |
percent_ceo_approval
|
Integer | 86 | |
String | MAP | Stock Ticker | |
ceo_rating
|
Float | 0.86 | |
ceo_ratings_count
|
Integer | 250 | |
business_outlook_rating
|
Float | 0.55 | |
compensation_and_benefits_rating
|
Float | 3.6 | |
culture_and_values_rating
|
Float | 3.8 | |
career_opportunities_rating
|
Float | 3.3 | |
recommend_to_friend_rating
|
Float | 0.75 | |
senior_management_rating
|
Float | 3.2 | |
work_life_balance_rating
|
Float | 3.5 | |
diversity_and_inclusion_rating
|
Float | 3.9 | |
diversity_and_inclusion_rating_count
|
Integer | 493 | |
job_count
|
Integer | 24 | |
salary_count
|
Integer | 945 | |
review_count
|
Integer | 1169 | |
interview_count
|
Integer | 77 | |
benefit_count
|
Integer | 356 | |
interview_experience_counts_positive
|
Integer | 54 | |
interview_experience_counts_negative
|
Integer | 8 | |
interview_experience_counts_neutral
|
Integer | 14 | |
String | Bristol, England|Mazatlán, Sinaloa|Webster, MA | Location Name | |
record_date
|
DateTime | 2023-04-18T00:00:00+00:00 |
Description
Country Coverage
History
Pricing
License | Starts at |
---|---|
One-off purchase |
€2,612 / purchase |
Monthly License | Available |
Yearly License | Available |
Usage-based |
€2,612 |
Suitable Company Sizes
Delivery
Use Cases
Categories
Related Searches
Related Products
Frequently asked questions
What is Web Scraping Data: Consumer/ Employee Sentiment Analysis Glassdoor and G2 Review Web Data?
Optimize your sentiment analysis with Wrapped.io’s comprehensive Web Scraping Data. Access 3+ years of detailed reviews from Glassdoor and G2. Sourced entirely from publicly available and non-sensitive information.
What is Web Scraping Data: Consumer/ Employee Sentiment Analysis Glassdoor and G2 Review Web Data used for?
This product has 5 key use cases. Wrapped.io recommends using the data for Market Research, Product development, Customer Experience Analysis, Employee Sentiment Analysis, and Consumer Sentiment Analysis. Global businesses and organizations buy Consumer Review Data from Wrapped.io to fuel their analytics and enrichment.
Who can use Web Scraping Data: Consumer/ Employee Sentiment Analysis Glassdoor and G2 Review Web 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 Wrapped.io to see what their data can do for your business and find out which integrations they provide.
How far back does the data in Web Scraping Data: Consumer/ Employee Sentiment Analysis Glassdoor and G2 Review Web Data go?
This product has 3 years of historical coverage. It can be delivered on a monthly, quarterly, and on-demand basis.
Which countries does Web Scraping Data: Consumer/ Employee Sentiment Analysis Glassdoor and G2 Review Web Data cover?
This product includes data covering 249 countries like USA, China, Japan, Germany, and India. Wrapped.io is headquartered in Lithuania.
How much does Web Scraping Data: Consumer/ Employee Sentiment Analysis Glassdoor and G2 Review Web Data cost?
Pricing for Web Scraping Data: Consumer/ Employee Sentiment Analysis Glassdoor and G2 Review Web Data starts at EUR2,612 per purchase. Connect with Wrapped.io to get a quote and arrange custom pricing models based on your data requirements.
How can I get Web Scraping Data: Consumer/ Employee Sentiment Analysis Glassdoor and G2 Review Web Data?
Businesses can buy Consumer Review Data from Wrapped.io and get the data via S3 Bucket, SFTP, Email, UI Export, REST API, SOAP API, Streaming API, and Feed API. Depending on your data requirements and subscription budget, Wrapped.io can deliver this product in .csv, .bin, .json, .xml, .xls, .sql, and .txt format.
What is the data quality of Web Scraping Data: Consumer/ Employee Sentiment Analysis Glassdoor and G2 Review Web Data?
You can compare and assess the data quality of Wrapped.io using Datarade’s data marketplace. Wrapped.io has received 1 review from clients.
What are similar products to Web Scraping Data: Consumer/ Employee Sentiment Analysis Glassdoor and G2 Review Web Data?
This product has 3 related products. These alternatives include InfoTrie Consumer Review & Product Data - 100M+ records, ratings & sentiment worldwide, In-Depth Consumer Review Data & Customer Experience Insights: Trustpilot, G2, & Capterra Data for Market Research Public Web Data, and CrawlBee Web Scraping Data Web Data Extraction Web Data Web Activity Data. 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.