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Text Analytics in Pega 7.2.2

Updated on June 20, 2018

The Pega 7 Platform provides the following techniques that you can use to process and structure text data from the Facebook, Twitter, and YouTube social media platforms:

  • Sentiment Analysis – Detect and analyze the feelings (attitudes, emotions, opinions) that characterize a unit of text.
  • Classification Analysis – Assign one or more classes or categories to a text sample to make that text easier to manage and sort.
  • Entity Extraction –  Extract named entities from text data and assign them to predefined categories, such as names of organizations, locations, people, quantities, or values.

To enable text analysis in your application, configure and customize the underlying infrastructure in the form of data sets, text analyzers, and data flows.

Try text analytics with NLP Sample

Use the NLP Sample application to explore the natural language processing capabilities of the Pega 7 Platform. The application is available as an archive that you can download and install. The application includes the following components:

  • The NLP Sample portal.
  • A set of rules that constitute the text analytics infrastructure in your application, including a sample text analyzer that supports sentiment, classification, and entity extraction analysis. 
  • A collection of taxonomies that demonstrate various use cases for classification analysis, for example, telecom, banking, customer service, and automobile.

You can use the provided rules and text samples for text analysis or you can configure your own rules and models to explore text classification, sentiment analysis, topic detection, and entity extraction.

Learn more

Configure text analyzer

Configure Text Analyzer rules to process content that is extracted from social media (Twitter, Facebook, and YouTube), emails, chat-bot messages, databases, REST APIs, customer support tickets, and so on. Use a variety of tools (such as lexicons, taxonomies, machine learning models) to customize the sentiment, classification, and entity extraction analysis that you want to apply to the text content that interests you. 


Build machine learning models

Create custom models for sentiment and classification analysis to implement machine learning into your text analysis. Using a wizard, you can perform the following actions:

  • Define the model details and the algorithm that the application uses to train the model: MaxEnt, Naïve Bayes, or support vector machine (SVM).
  • Upload the training and testing sample.
  • Review the model configuration and create the model.
  • View the accuracy score of the model and export that model or upload it as part of decision data for use in text analyzers.

By uploading machine learning models as part of text analyzer rules, you can enhance the accuracy with which text analyzer rules detect sentiment or classify text.

Learn more


Create and configure data sets for text content

Retrieve the text content that interests you from a variety of sources. You can retrieve text content from Facebook, Twitter, and YouTube social media platforms, emails, or databases, and analyze it in your application.


Customize metadata retrieval

Optionally, if you are analyzing text content from Facebook, Twitter, or YouTube, you can customize your social media data set to retrieve additional metadata, such as user verification information, profile pictures, icons, or other information that is relevant to achieving your business goal. You can configure the metadata retrieval criteria on the Social Media Metadata landing page that is available in applications that have access to the Pega-NLP ruleset.

Combine and process

Arrange the text analyzer and other rules that you created into a processing pattern of a data flow or a process flow.

Data flows offer a flexible solution for combining all your data points into a processing pattern that has a source from which the input data is taken and a destination to which the results are saved. Between the source and destination, you can apply various processing instructions in the form of different shapes. In a data flow that is designed for text analytics of Facebook, Twitter, or YouTube, you can reference a social media data set as the source, apply processing instructions in the form of text analyzers, and save the results into a database, activity, or a JSON file. You can also enrich your data flow with additional shapes, such as filters, to process only negative content or the content from the most influential users, and so on.

You can also reference a text analyzer rule in a process flow by using the Utility shape to analyze the text content of emails or customer support tickets.


Have a question? Get answers now.

Visit the Support Center to ask questions, engage in discussions, share ideas, and help others.

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