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What is DiscoverText?

DiscoverText combines collaborative text analytics for both machine learning and human input, offering a comprehensive suite of multilingual text mining and data science functionalities alongside human annotation and machine-learning features. Our cloud-based software tools empower users to efficiently and precisely analyze extensive text datasets, making it easier for customers to organize unstructured text typically encountered in market research. In addition, our platform enables users to incorporate relevant metadata from various sources such as customer feedback systems, CRMs, and chat interactions. Learn why we are the top choice in text and metadata analysis, as well as social network analysis support, with a trusted reputation among numerous academic research groups. With the capability to construct machine-learning classifiers in mere hours or even minutes through crowdsourcing, our service stands out in the industry. Moreover, we offer an API and facilitate seamless technical integrations with platforms like SurveyMonkey and Twitter, enhancing the overall user experience. Our dedication to innovation and customer satisfaction continues to drive our success.

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

Azure Marketplace
SurveyMonkey
TAS Insight Engine
Unremot
WordPress
X (Twitter)

Integrations Supported

Azure Marketplace
SurveyMonkey
TAS Insight Engine
Unremot
WordPress
X (Twitter)

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

Texifter

Date Founded

2009

Company Location

United States

Company Website

discovertext.com

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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