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Configuring topic detection

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Detect topics (talking points) of the text to automatically classify user queries and shorten customer service response times.

  1. In the navigation panel, click Records Decision Text Analyzer .

  2. Open the Text Analyzer rule that you want to edit.

  3. In the Text categorization section, select the Enable topic detection check box.

  4. In the Topic model field, press the Down arrow key to specify the primary model that you want to use for topic detection.

    The model that addresses your business use case.
  5. Optional:

    To add more topic detection models, click Add topic model and press the Down arrow key to select a model.

    The small talk detection model.
  6. Select the topic preference by enabling one of the following options:

    • Exclude the rule-based models from analysis by selecting Always use rule based topics. Select this option when no machine-learning model is associated with the rule or when the keywords-based topic detection analysis provides more reliable results than the machine-learning model.
    • Include machine-learning output in the analysis by selecting Use model based topics if available.
  7. Optional:

    Configure language detection preferences.

    Perform this step to analyze content that is in more than one language and configure your application to always detect the specified language. For more information, see Configuring language detection preferences.
  8. Optional:

    To increase topic detection accuracy, enable checking spelling.

    For more information, see Configuring spelling checker settings.
    If you enable the spelling checker, you might experience increased memory consumption in your application.
  9. Confirm your settings by clicking Save.

  • 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.

  • 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.

  • Configuring advanced text analytics settings

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

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