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Analyzing natural language

Updated on July 5, 2022

Effortlessly analyze and extract meaningful information from large volumes of text with the use of text analytics. Based on your findings, you can further improve business performance and customer experience.

With text analytics you can analyze and structure text, in multiple languages, that comes in through various channels, such as emails, social media platforms, and chat channels.

  • Language support for NLP

    Pega Platform provides text analytics based on natural language processing (NLP) that you can use to detect, process, and structure text data from email, chatbots, and social media platforms. Depending on the language of the analyzed content, various text analytics features help you obtain accurate analysis results.

  • Out-of-the-box text analytics models

    Pega Platform provides trained and ready-to-use text analytics models.

  • Analyzing messages with text predictions

    Text predictions use natural language processing (NLP), predictive and adaptive analytics, and artificial intelligence to analyze incoming messages in your conversational channels, such as email or chat. Text analytics can help you route work, populate properties in business cases, and respond to users with relevant messages.

  • Parsing emails

    Pega Platform provides the pxEmailParser model that you can use as a preprocessing model to analyze incoming emails and parse their content into logical components: body, signature, and disclaimer. You can define the components on which you want to perform text analysis and which you want to exclude from analysis.

  • Labeling text with categories

    Efficiently analyze large volumes of text and assign each sentence to a predefined category by using the text categorization feature. With text categorization, you can quickly react to what your customers are saying and aptly address their inquiries or concerns.

  • Extracting keywords and phrases

    Use Prediction Studio to create entity extraction models for text analytics. With text extraction, you can detect named entities from text data and assign them to predefined categories, such as names of organizations, locations, people, quantities, or values.

  • Building text analyzers

    Text analyzer rule provides sentiment, categorization, text extraction, and intent analysis of text-based content such as emails and chat messages.

  • Managing text analytics models

    Data scientists can perform various housekeeping activities for sentiment and text classification models in the Predictions work area in Prediction Studio. The range of available activities depends on whether the model has been built (the displayed model status is Completed) or is incomplete (the displayed model status is In build).

  • Sentiment lexicons

    A sentiment lexicon is a list of semantic features for words and phrases. Use lexicons for creating machine learning-based sentiment and intent analysis models.

  • Text analytics accuracy measures

    Models predict an outcome, which might or might not match the actual outcome. The following measures are used to examine the performance of text analytics models. When you create a sentiment or classification model, you can analyze the results by using the performance measures that are described below.

  • Customizable Interaction API for text analytics

    The Interaction API provides a customizable interface for handling interactions through conversational channels such as email channels and instant messaging platforms.

  • Natural language processing reference

    Review the following articles for best practices for natural language processing (NLP) in , including such operations as configuring and training text analytics models, using the NLP Sample application and the text analytics APIs.

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