Skip to main content

This content has been archived and is no longer being updated.

Links may not function; however, this content may be relevant to outdated versions of the product.

Building text analyzers

Suggest edit Updated on April 5, 2022

Text analyzer rule provides sentiment, categorization, text extraction, and intent analysis of text-based content such as news feeds, emails, and postings on social media streams including Facebook, and YouTube.

Text analyzers provide a combined set of powerful natural language processing (NLP) tools to ingest all text-based content, parse unstructured data into structured elements, and deliver actionable items. For example, by using the Pega Platform NLP capabilities, you can intelligently process emails in your application to deliver automatic responses to users, depending on the intent that the text analyzer detected in the user query.

You can use machine learning models in text analyzers to perform language processing tasks automatically, for example, to predict sentiment, assign topics and intents, detect entities, and so on. For more information about machine learning in Pega Platform, see Prediction Studio overview.

The Text Analyzer rule is available in applications that have access to the decision management rulesets along with the Pega-NLP ruleset or in applications built on that ruleset.

Did you find this content helpful? YesNo

100% found this useful

Have a question? Get answers now.

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

We'd prefer it if you saw us at our best.

Pega.com is not optimized for Internet Explorer. For the optimal experience, please use:

Close Deprecation Notice
Contact us