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What is NetOwl TextMiner?

NetOwl TextMiner combines the renowned NetOwl Extractor with Elasticsearch to provide a cutting-edge text analytics platform. This software leverages the comprehensive capabilities of NetOwl, making it ideal for executing "what if" scenarios, engaging in discovery processes, supporting rapid investigations, and conducting in-depth research. By integrating all the text analytics tools from the NetOwl Extractor—such as entity extraction, relationship and event extraction, sentiment analysis, text categorization, and geotagging—TextMiner offers a robust text mining solution. The outcomes produced by the Extractor are efficiently stored within Elasticsearch, which enhances the system with a variety of intelligent search and analytical functions. The collaboration between Elasticsearch and NetOwl guarantees swift and scalable real-time text analysis, making it suitable for managing Big Data projects. Additionally, TextMiner features a user-friendly web interface that can be easily customized to meet diverse analytical requirements, allowing users to quickly extract the most pertinent insights from large text collections. This flexibility not only improves user experience but also facilitates more specialized analyses across different fields, catering to the unique needs of various industries.

What is Azure Text Analytics?

Harness natural language processing to gain valuable insights from unstructured text without requiring any machine learning knowledge, by utilizing an array of features from the Cognitive Services for Language. Elevate your understanding of customer emotions through sentiment analysis and identify key phrases and entities such as people, places, and organizations to uncover common themes and patterns. Use specialized, pretrained models to classify medical terminology specific to various fields. Evaluate text across multiple languages and reveal essential concepts within the content, which include key phrases and named entities that highlight individuals, events, and organizations. Delve into customer feedback regarding your brand while examining sentiments linked to specific topics through opinion mining techniques. Additionally, derive critical insights from unstructured clinical documents, including doctors' notes, electronic health records, and patient intake forms, by applying text analytics tailored for healthcare settings, ultimately enhancing patient care and informing decision-making processes. By integrating these advanced capabilities, organizations can stay ahead of trends and better meet the needs of their stakeholders.

Media

Media

Integrations Supported

ArcGIS
Azure Marketplace
Elasticsearch
Google Maps
IBM Cloud
Kibana
MarkLogic
Palantir Apollo
SolrCommerce
TAS Insight Engine
Tableau
Unremot

Integrations Supported

ArcGIS
Azure Marketplace
Elasticsearch
Google Maps
IBM Cloud
Kibana
MarkLogic
Palantir Apollo
SolrCommerce
TAS Insight Engine
Tableau
Unremot

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

NetOwl

Date Founded

1996

Company Location

United States

Company Website

www.netowl.com/text-mining

Company Facts

Organization Name

Microsoft

Date Founded

1975

Company Location

United States

Company Website

azure.microsoft.com/en-us/services/cognitive-services/text-analytics/

Categories and Features

Text Mining

Boolean Queries
Document Filtering
Graphical Data Presentation
Language Detection
Predictive Modeling
Sentiment Analysis
Summarization
Tagging
Taxonomy Classification
Text Analysis
Topic Clustering

Categories and 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

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