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• Insights from your customers • Multimedia, Multi-lingual Support • Content Classification • Relationship Graphs • Best of Google Deep Learning models
PAT Rating™
Editor Rating
Aggregated User Rating
Rate Here
Ease of use
8.0
6.5
Features & Functionality
7.9
7.0
Advanced Features
8.1
6.5
Integration
7.9
8.9
Performance
8.1
8.4
Customer Support
8.0
7.9
Implementation
9.1
Renew & Recommend
10
Bottom Line
Extract actionable insights on product reception or user experience from customer conversations in email, chat or social media by using entity detection and sentiment analysis.
8.0
Editor Rating
7.7
Aggregated User Rating
25 ratings
You have rated this
Google Cloud Natural Language API reveals the structure and meaning of text by offering powerful machine learning models in an easy to use REST API. You can use it to extract information about people, places, events and much more, mentioned in text documents, news articles or blog posts.
You can use it to understand sentiment about your product on social media or parse intent from customer conversations happening in a call center or a messaging app. You can analyze text uploaded in your request or integrate with your document storage on Google Cloud Storage.
Extract actionable insights on product reception or user experience from customer conversations in email, chat or social media by using entity detection and sentiment analysis. Filter inappropriate content, classify documents by topics, and build relationship graphs of entities extracted from news or wikipedia articles, by using signals from the state of the art syntax analysis.
Extract actionable insights on product reception or user experience from customer conversations
Company size
Medium (50 to 1000)
User Role
Super User
User Industry
Health care
Rating
Ease of use8.4
Features & Functionality8.2
Advanced Features8.3
Integration8.1
Performance8.1
Implementation8.1
ADDITIONAL INFORMATION Google Cloud Natural Language API offers you a nice machine learning models in a very easy to use REST API. You can extract actionable insights on product reception or user experience from customer conversations in email, chat or social media with the use of a very powerful entity detection and sentiment analysis. Users can even filter inappropriate content, classify documents by topics, and build relationship graphs of entities extracted from news or wikipedia articles with the use of signals from the state of the art syntax analysis so you need not worry about age appropriateness or censorship, you can cater to all ages. The Google Cloud Natural Language API also enables you to easily analyze text in multiple languages including English, Spanish, Japanese, Chinese (Simplified and Traditional), French, German, Italian, Korean and Portuguese. You can even extract tokens and sentences from such languages to identify its parts of speech (PoS) for you to create dependency parse trees for each sentence.
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Extract actionable insights on product reception or user experience from customer conversations
Medium (50 to 1000)
Super User
Health care
ADDITIONAL INFORMATION
Google Cloud Natural Language API offers you a nice machine learning models in a very easy to use REST API. You can extract actionable insights on product reception or user experience from customer conversations in email, chat or social media with the use of a very powerful entity detection and sentiment analysis. Users can even filter inappropriate content, classify documents by topics, and build relationship graphs of entities extracted from news or wikipedia articles with the use of signals from the state of the art syntax analysis so you need not worry about age appropriateness or censorship, you can cater to all ages. The Google Cloud Natural Language API also enables you to easily analyze text in multiple languages including English, Spanish, Japanese, Chinese (Simplified and Traditional), French, German, Italian, Korean and Portuguese. You can even extract tokens and sentences from such languages to identify its parts of speech (PoS) for you to create dependency parse trees for each sentence.