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Description
Amazon Comprehend is an innovative natural language processing (NLP) tool that employs machine learning techniques to extract valuable insights and connections from text without requiring any prior machine learning knowledge.
Your unstructured data holds a wealth of possibilities, with sources like customer emails, support tickets, product reviews, social media posts, and even advertising content offering critical insights into customer sentiments that can drive your business forward. The challenge lies in how to effectively tap into this rich resource. Fortunately, machine learning excels at pinpointing specific items of interest within extensive text datasets—such as identifying company names in analyst reports—and can also discern the underlying sentiments in language, whether that involves recognizing negative reviews or acknowledging positive interactions with customer service representatives, all at an impressive scale.
By leveraging Amazon Comprehend, you can harness the power of machine learning to reveal the insights and relationships embedded within your unstructured data, empowering your organization to make more informed decisions.
Description
An all-encompassing domain data source provides access to website logos, company information, categorization, and additional features derived from a URL or email. Our website categorization API boasts high accuracy, allowing a straightforward search of a business to classify its sector among 385 distinct topic categories. This classification framework adheres to the IAB V2 standard and can be utilized for personalized marketing, audience segmentation, online filtering, and various other applications. With three primary category structures available, users can choose between the IAB taxonomy's detailed categorization or opt for a simpler framework that suits their needs. Our advanced API leverages a machine learning engine to analyze a website's content and metadata, extracting relevant text to classify the site effectively while assigning up to three categories, all enhanced through natural language processing techniques. This versatility ensures that businesses can tailor their digital strategies more effectively by leveraging precise categorization capabilities.
API Access
Has API
API Access
Has API
Integrations
AWS AI Services
AWS App Mesh
AWS Lambda
Amazon Comprehend Medical
Amazon S3
Amazon Web Services (AWS)
Axon Ivy
Camunda
DataMorf
Datasaur
Integrations
AWS AI Services
AWS App Mesh
AWS Lambda
Amazon Comprehend Medical
Amazon S3
Amazon Web Services (AWS)
Axon Ivy
Camunda
DataMorf
Datasaur
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
$89 per month
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Amazon
Founded
1994
Country
United States
Website
aws.amazon.com/comprehend/
Vendor Details
Company Name
Klazify
Founded
2016
Country
United Kingdom
Website
www.klazify.com
Product Features
Natural Language Processing
Co-Reference Resolution
In-Database Text Analytics
Named Entity Recognition
Natural Language Generation (NLG)
Open Source Integrations
Parsing
Part-of-Speech Tagging
Sentence Segmentation
Stemming/Lemmatization
Tokenization
Text Mining
Boolean Queries
Document Filtering
Graphical Data Presentation
Language Detection
Predictive Modeling
Sentiment Analysis
Summarization
Tagging
Taxonomy Classification
Text Analysis
Topic Clustering
Product Features
Text Mining
Boolean Queries
Document Filtering
Graphical Data Presentation
Language Detection
Predictive Modeling
Sentiment Analysis
Summarization
Tagging
Taxonomy Classification
Text Analysis
Topic Clustering