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"Integrate advanced text analytics into your predictive models"

Thomas_OttThomas_Ott RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 1,761 Unicorn
edited June 2019 in Knowledge Base

By: Antonio Matarranz, CMO of MeaningCloud

 

MeaningCloud has released a new RapidMiner Extension that provides a high-quality multilingual text mining, thanks to its broad analytical functions and customizability.

 

Would you like to extract the information underlying unstructured text -from documents, customer interactions and social comments-, combine it with structured data and incorporate it into your analytic models based on RapidMiner?

 

The new RapidMiner Extension for MeaningCloud gives users the ability to structure all types of text and extract its meaning. It provides RapidMiner users with a set of operators performing some of MeaningCloud’s most popular functions: entity and concept extraction, theme classification using standard taxonomies, sentiment analysis, and lemmatization.

 

MeaningCloud Extension RapidMiner.png

 

 

 

 

 

More importantly, MeaningCloud has incorporated powerful customization tools that enable users to adapt it to their application domain (e.g. analysis of the voice of the customer in the financial industry) through the creation of personal dictionaries and classification and sentiment models. These capabilities are unique in the industry and ensure high levels of precision and recall.

Practical applications of this Extension range from root cause analysis in customer surveys to fraud or churn prevention.

 

Download it from RapidMiner Marketplace or MeaningCloud website.

 

Learn how to use the Extension in this recorded webinar

Would you like to see the MeaningCloud Extension in action, in a real-life scenario that combines structured data and text analytics? Learn more about application scenarios? Check out this recorded webinar.

 

Build your first text+data models in a snap using these tutorials

Use these two tutorials to learn how to extract insights that combine structured data with unstructured text. We use a dataset of food reviews from Amazon, including numeric scores and free text verbatims, to

  1. Analyze sentiment from the text and assess its correlation with numeric scores (see tutorial).
  2. Extract topics from the text and use them to induct a rule-based model to predict sentiment (see tutorial).

All data, analytics workflows, models, and results are available for download from the tutorials. Happy analyzing!

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Author:

Antonio Matarranz, CMO of MeaningCloud.

 

Antonio is an engineer become marketer. He holds a master's degree in Electronic Engineering from the Technical University of Madrid, a MBA from the IE Business School and a Executive Certificate in Marketing & Sales Management from the Kellogg School of Management.

 

Connect with Antonio on LinkedIn.

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