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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

Description

Komprehend AI offers an extensive range of document classification and NLP APIs designed specifically for software developers. Our advanced NLP models leverage a vast dataset of over a billion documents, achieving top-notch accuracy in various common NLP applications, including sentiment analysis and emotion detection. Explore our free demo today to experience the effectiveness of our Text Analysis API firsthand. It consistently delivers high accuracy in real-world scenarios, extracting valuable insights from open-ended text data. Compatible with a wide range of industries, from finance to healthcare, it also supports private cloud implementations using Docker containers or on-premise deployments, ensuring your data remains secure. By adhering to GDPR compliance guidelines meticulously, we prioritize the protection of your information. Gain insights into the social sentiment surrounding your brand, product, or service by actively monitoring online discussions. Sentiment analysis involves the contextual examination of text to identify and extract subjective insights from the material, thereby enhancing your understanding of audience perceptions. Additionally, our tools allow for seamless integration into existing workflows, making it easier for developers to harness the power of NLP.

Description

SentioAI is an innovative technology solution that leverages natural language processing, machine learning, and predictive analytics to swiftly and accurately pinpoint the most pertinent documents from a vast array. By addressing the classification challenges inherent in Big Data through its unique proprietary methods, SentioAI outperforms other technologies, providing quicker and more precise results while also being cost-effective. The system ranks documents from the most to least relevant, allowing users to review and tag a small subset of the dataset. This tagged data trains SentioAI's prediction engine, which continuously enhances its accuracy with each new document added. The system intelligently assesses when the training phase is complete and subsequently applies its models to the entire dataset to produce comprehensive results. Ultimately, SentioAI not only accelerates the document retrieval process but also ensures that users receive the most reliable information efficiently.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Docker Yes 
Quickwork Yes 
Unremot Yes 

Integrations

Docker No 
Quickwork No 
Unremot No 

Pricing Details

$79 per month
Free Trial No 
Free Version Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises Yes 
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) Yes 
In Person Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Komprehend

Country

India

Website

komprehend.io

Vendor Details

Company Name

RINA Systems

Country

United States

Website

www.rinasystems.com/product/sentioAI.html

Product Features

Emotion Recognition

Facial Emotions No 
Facial Expression Analysis No 
Machine Learning No 
Photo Emotions No 
Speech Emotions No 
Video Emotions No 
Written Text Emotions No 

Natural Language Processing

Co-Reference Resolution No 
In-Database Text Analytics No 
Named Entity Recognition No 
Natural Language Generation (NLG) No 
Open Source Integrations No 
Parsing No 
Part-of-Speech Tagging No 
Sentence Segmentation No 
Stemming/Lemmatization No 
Tokenization No 

Text Mining

Boolean Queries No 
Document Filtering No 
Graphical Data Presentation No 
Language Detection No 
Predictive Modeling No 
Sentiment Analysis No 
Summarization No 
Tagging No 
Taxonomy Classification No 
Text Analysis No 
Topic Clustering No 

Product Features

Natural Language Processing

Co-Reference Resolution No 
In-Database Text Analytics No 
Named Entity Recognition No 
Natural Language Generation (NLG) No 
Open Source Integrations No 
Parsing No 
Part-of-Speech Tagging No 
Sentence Segmentation No 
Stemming/Lemmatization No 
Tokenization No 

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