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Building text analyzers

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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.

  • Topic detection

    This type of text analysis determines the topics to which a text unit should be assigned. In Pega Platform, topic detection is achieved by means of machine learning-based and keyword-based models. By categorizing text into topics, you can make it easier to manage and sort, for example, you can group related queries in customer support.

  • Sentiment analysis

    Sentiment analysis determines whether the analyzed text expresses a negative, positive, or neutral opinion. By analyzing the content of a text sample, it is possible to estimate the emotional state of the writer of the text and the effect that the writer wants to have on the readers. Sentiment analysis in Pega Platform combines the lexicon-based and machine learning-based approaches to predict the polarity of the analyzed text.

  • Text extraction analysis

    Text extraction analysis is the process of extracting named entities from unstructured text such as press articles, Facebook posts, or tweets, and categorizing them. Typically, a named entity is a proper noun that falls into a commonly understood category such as a person, organization, or location. An entity can also be a Social Security number, email address, postal code, and so on.

  • Intent analysis

    Through intent analysis, you can determine the expressed intent of your customers or product reviewers.

  • Configuring advanced text analytics settings

    Configure language detection settings, enable spell checking, and control how the text is categorized, based on various criteria.

  • Testing Text Analyzer rules

    You can test the performance of a Text Analyzer rule after you configured that rule to perform natural language processing tasks that fulfill your business requirements.

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