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What is Kimola Cognitive?

Kimola Cognitive stands out as a dependable Machine Learning Platform, enabling users to effortlessly gather reviews from more than 20 various sources while also analyzing and classifying customer feedback and other types of textual information. Key features of Kimola Cognitive include: - Web scraping capabilities to collect reviews - User-friendly text analysis and entity recognition through a drag-and-drop interface - Access to pre-built models in the Kimola Cognitive Gallery for data analysis - The ability to create, train, and save custom machine learning models without any coding required - Integration with GPT to produce executive summaries, SWOT analyses, and other influential marketing documents - Multilingual support available in six languages and continually expanding to reach a wider audience. This platform not only enhances efficiency but also democratizes access to sophisticated analytical tools for users of all backgrounds.

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

Amazon
Azure Marketplace
TAS Insight Engine
Trustpilot
Unremot
Yelp

Integrations Supported

Amazon
Azure Marketplace
TAS Insight Engine
Trustpilot
Unremot
Yelp

API Availability

Has API

API Availability

Has API

Pricing Information

$199 / 10000 Queries / month
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

Kimola

Date Founded

2014

Company Location

United States

Company Website

kimola.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

Artificial Intelligence

Chatbot
For Healthcare
For Sales
For eCommerce
Image Recognition
Machine Learning
Multi-Language
Natural Language Processing
Predictive Analytics
Process/Workflow Automation
Rules-Based Automation
Virtual Personal Assistant (VPA)

Qualitative Data Analysis

Annotations
Collaboration
Data Visualization
Media Analytics
Mixed Methods Research
Multi-Language
Qualitative Comparative Analysis
Quantitative Content Analysis
Sentiment Analysis
Statistical Analysis
Text Analytics
User Research Analysis

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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