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What is Watson Natural Language Understanding?

Watson Natural Language Understanding is a cloud-based solution that utilizes advanced deep learning methods to extract metadata from text, such as entities, keywords, categories, sentiment, emotions, relationships, and syntactic structures. Engage deeply with your data through text analysis, allowing for the extraction of keywords, concepts, categories, and beyond. This service can analyze unstructured data in more than thirteen different languages. With its pre-built machine learning models for text mining, it achieves an impressive level of accuracy in processing your content. You have the flexibility to deploy Watson Natural Language Understanding behind your firewall or on any preferred cloud platform. Tailor Watson to understand the unique language of your business and obtain customized insights through Watson Knowledge Studio. Your ownership of data is safeguarded, as we prioritize the security and confidentiality of your information, ensuring that IBM will not collect or retain your data. By leveraging our advanced natural language processing (NLP) tools, developers can effectively decipher and extract significant insights from their unstructured data, thereby improving their decision-making processes. This cutting-edge method not only simplifies data analysis but also empowers organizations to fully exploit their information resources, leading to better strategic outcomes. Organizations that embrace this technology are likely to see enhanced efficiency and innovation across their operations.

What is Verint Text Analytics?

Unstructured information, including emails, chat messages, and phone notes, can be leveraged to derive valuable insights that enhance your customer experience (CX). By utilizing multichannel analytics, you can gain deeper insights into customers who interact through various platforms. These multichannel customers necessitate a holistic approach to employee and customer analytics across different channels. While voice communication continues to be significant, there is a noticeable shift as consumers increasingly prefer digital avenues such as chat, email, and social media. Incorporating Text Analytics provides a comprehensive evaluation of your customer engagement strategy, ensuring that no aspect is overlooked. This integration ultimately empowers businesses to make data-driven decisions that enhance interactions and foster loyalty.

Media

Media

Integrations Supported

TAS Insight Engine

Integrations Supported

TAS Insight Engine

API Availability

Has API

API Availability

Has API

Pricing Information

$0.003 per NLU item
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

IBM

Date Founded

1911

Company Location

United States

Company Website

www.ibm.com/cloud/watson-natural-language-understanding

Company Facts

Organization Name

Verint

Date Founded

1994

Company Location

United States

Company Website

www.verint.com/text-analytics/

Categories and Features

Emotion Recognition

Facial Emotions
Facial Expression Analysis
Machine Learning
Photo Emotions
Speech Emotions
Video Emotions
Written Text Emotions

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

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

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