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Configuring topic settings in text predictions

Use topic settings to control how text is categorized, depending on the selected level of classification granularity. You can adjust text categorization according to your business needs, for example, to change the granularity of the analysis to document level if you analyze short tweets.

  1. Open the text prediction:

    1. In the navigation pane of App Studio, click Channels.

    2. In the Current channel interfaces section, click the icon that represents a channel for which you want to configure the text prediction.

    3. On the channel configuration page, click the Behavior tab, and then click Open text prediction.

  2. In the Prediction workspace, click the Settings tab, and scroll down to the Topic settings section.

  3. In the Pega NLP section, configure granularity for Pega NLP models:

    Choices Actions
    Define sentence-level granularity

    In the Pega NLP section, select Sentence level.

    Sentence-level granularity is optimal for high-precision analysis when you want to identify the top topic for each sentence separately. Use this feature to analyze large units of text, for example, emails or blog entries.

    Define document-level granularity
    1. In the Pega NLP section, select Document level, and then click Configure.

    2. In the Document level granularity window, select one of the following options:

      • To detect only a specified number of topics that received the highest confidence score, select Select an amount of top topics, and then enter the appropriate amount.
      • To limit the number of detected topics to only those above a specific confidence score threshold, select Select topics above a confidence score threshold, and then enter the appropriate threshold.

      Document-level granularity is useful when you want to categorize the text as a whole, with no further breakdown. Use this feature to analyze smaller units of text, for example, Facebook posts or tweets.

    3. Optional:

      Enable the option to fall back to rule-based topic detection if the specified confidence threshold is not met.

    4. Click Submit.

    Configuring the confidence score threshold and fallback setting
  4. In the External models section, configure granularity for external models:

    • To detect only a specific number of topics that received the highest confidence score, select Select an amount of top topics, and then enter the number of topics to detect.
    • To limit the number of detected topics to only those above a specific confidence score threshold, select Select topics above a confidence score threshold, and then enter a threshold value.
  5. In the Taxonomy section, in the Parent topic field, enter a parent topic for all the topics that you add to this prediction.

    By default, the parent topic is set to action.
  6. Click Save.

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