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Kimola Cognitive: Alternative to Amazon Comprehend

Kimola Cognitive is a no-code machine learning tool that gathers, analyzes, and classifies customer feedback to help you grow your brand.

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Tool Highlights

A brief and concise overview of this tool at a single glance

Analyze consumer reviews or text data with pre-built machine learning models—no coding necessary
Alternative to Amazon Comprehend, MonkeyLearn, and Chattermill
Create your own custom machine learning model by using a simple drag-and-drop user interface
Best for: Marketers, entrepreneurs, and product managers who want a machine learning platform to streamline text data analysis

Tool Type

SaaS, Software

Tool Category

Artificial Intelligence, Client Management, Productivity

Pricing Model

Free Trial

Video Source: AppSumo Official YouTube Channel

Tool Description

A detailed overview of this tool to learn more about it

Kimola Cognitive makes it easy to analyze consumer reviews, products, services, and topics, so you can build better marketing strategies. Use the drag-and-drop interface to analyze text in over six languages all without any coding.

The browser extension allows you to create consumer review datasets (aka “airsets”) from supported media on the Internet. Classifying online conversations and analyzing sentiment is also a breeze! Just scrape comments on mediums like YouTube, Tripadvisor, AppSumo, and G2 to get started.

Choose from a gallery of ready-to-use machine learning models to monitor data like customer sentiment and hate speech analysis. Plus, Kimola Cognitive is constantly publishing new machine learning models and keeping them up-to-date for different business needs.

So whether you’re a corporate professional or social media influencer, you can automatically classify consumer reviews and leverage those insights for your brand.

Tool Features

Explore the main features of this powerful tool.

Text Analysis with Entity Recognition: Kimola Cognitive offers text analytics to reveal insights that are mentioned in consumer reviews such as organizations, celebrities, interests, work of arts, locations, nutrition, race & ethnicity, diseases. Just drag & drop consumer reviews in excel file and let the magic happen in minutes!
Pre-Built Models that Fit Your Business: Kimola Cognitive comes with a gallery of ready-to-use Machine Learning models for the most common use cases like sentiment and hate speech analysis along with consumer conversations around SaaS products, mobile apps, and games.
Create & Store Custom Machine Learning Models: Every research is a different journey to understand humankind, so requirements will usually be unique. That's why Kimola Cognitive also supports creating custom Machine Learning models by training your own dataset.
Hate Speech Detection: This model is prepared to identify hate speech towards a specific ethnicity, nationality, religion, sexuality, disability, or class and prevent this kind of malicious intent in the media.
Consumer Sentiment Classifier: This model is created with the intent of classifying positive, negative, or neutral sentiment in any consumer conversation.
Mobile Game Conversations Classifier: This model is created specifically for the Mobile Gaming Industry. The model has 4 main categories and helps marketing professionals to understand mobile gamers better.
E-Commerce Conversations Classifier: This model is created specifically for the E-Commerce Industry. The model has 4 main categories and helps marketing professionals to understand online shopping consumers better.
SaaS Conversations Classifier: This model is created specifically for the Saas Products. The model has 4 main categories and helps marketing professionals to understand saas product consumers better.
Horeca Conversations Classifier: This model is created specifically for the Horeca Industry. The model has 4 main categories and helps marketing professionals to understand hotel-restaurant-cafe consumers better.
Mobile App Conversations Classifier: This model is created specifically for the Mobile App Industry. The model has 4 main categories and helps app developers to understand mobile app users better.
Common Consumer Conversation Classifier: This model is created with the intent of classifying consumer conversation around a product, service, or brand in general.
Banking Conversations Classifier: This model is created specifically for the Banking Industry. The model has 5 main categories and helps marketing professionals to understand banking consumers better.
Automotive Conversations Classifier: This model is created specifically for the Automotive Industry. The model has 5 main categories and helps marketing professionals to understand automotive consumers better.
Telecommunication Conversations Classifier: This model is created specifically for Mobile Network Operators. The model has 5 main categories and helps marketing professionals to understand telco consumers better.
Patient and Caregiver Conversations Classifier: This model is created specifically for the Health Industry. It helps researchers label texts by the source. The sources are considered as “patient” and “caregiver” as source-based research in Health is very important to understand the motivations and problems of patients and caregivers separately.
News Classification: This model classifies any text content in terms of news categories to categorize news articles or classify consumer conversations.

Tool Resources

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