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Description
Our natural language processing technology efficiently reveals insights from vast amounts of text with an unparalleled level of accuracy in the market, while offering the adaptability to fit seamlessly into your unique business and operational workflows. You can achieve significant and consistent outcomes with this approach. A considerable number of our deployed models exceed 90% in both precision and recall, standing in stark contrast to the industry's average of 68%. This remarkable performance explains why Amenity's NLP is relied upon by major organizations across various sectors. It focuses on extracting relevant information while filtering out irrelevant noise. Our NLP processes, refines, and organizes text from millions of documents, converting them into actionable insights for your business. It assesses the context and significance of text, recognizing shifts in meaning from individual phrases to broader paragraphs. Additionally, it allows you to transform any text source into tailored industry analyses and sentiments linked to business activities, providing clarity on the reasons behind the outcomes. With a commitment to transparency, our NLP is customizable to meet your specific needs and adapt to any environment. Ultimately, this ensures you receive the insights that matter most to your business.
Description
SAS Text Miner allows for the extraction of insights from a variety of text documents, revealing underlying themes and concepts. This tool effectively integrates quantitative data with unstructured text, merging text mining with conventional data mining approaches. As part of the SAS® Enterprise Miner suite, it necessitates that SAS Enterprise Miner is installed on the same system. Additionally, SAS High-Performance Text Mining can operate on either a computer grid or a single machine equipped with multiple CPUs. The text algorithms employed are designed to be multi-threaded and work in-memory, significantly enhancing both responsiveness and concurrency while minimizing input/output strain. Users can access SAS Text Miner as nodes within the SAS High-Performance Data Mining framework or utilize it through the procedures PROC HPTMINE and PROC HPTMSCORE. To quickly grasp SAS technology, individuals can benefit from courses offered by analytics professionals, ensuring they gain a comprehensive understanding of the tools available. Enhancing one’s knowledge in this area can lead to greater proficiency in data analysis and mining techniques.
API Access
Has API
No
API Access
Has API
No
Integrations
No details available.
Integrations
No details available.
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
Yes
Vendor Details
Company Name
Amenity Analytics
Country
United States
Website
www.AmenityAnalytics.com
Vendor Details
Company Name
SAS Institute
Founded
1976
Country
United States
Website
support.sas.com/en/software/text-miner-support.html
Product Features
Text Mining
Boolean Queries
No
Document Filtering
No
Graphical Data Presentation
No
Language Detection
No
Predictive Modeling
No
Sentiment Analysis
Yes
Summarization
No
Tagging
No
Taxonomy Classification
No
Text Analysis
Yes
Topic Clustering
Yes
Product Features
Text Mining
Boolean Queries
Yes
Document Filtering
Yes
Graphical Data Presentation
No
Language Detection
Yes
Predictive Modeling
Yes
Sentiment Analysis
Yes
Summarization
No
Tagging
No
Taxonomy Classification
Yes
Text Analysis
No
Topic Clustering
Yes
