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Setting up a keyword-based topic detection model

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Create a keyword-based topic detection model by specifying the model name, language, and corresponding ruleset. After you create the model, complete the model configuration by defining a taxonomy of topics and keywords.

  1. In the navigation pane of Prediction Studio, click Models.

  2. In the header of the Models work area, click New Text categorization .

  3. In the New text categorization model window, perform the following actions:

    1. In the Name field, enter a name for the topic detection model.

    2. In the Language list, select a language for the model to use.

      For more information, see Language support for NLP.
    3. In the What do you want to detect? section, click Topics, and then select the Use category keywords check box.

    4. In the Save taxonomy section, specify the class in which you want to save the model, and then specify its ruleset or branch.

    5. Click Create.

Complete the model configuration by either creating a taxonomy in Prediction Studio, or by importing a file with an existing taxonomy. For more information, see Creating a taxonomy for keyword-based topic detection or Importing a taxonomy for keyword-based topic detection.

  • Creating keyword-based topic detection models

    Efficiently connect your customers with the right consultant without having to provide training data to the topic detection model. Instead, you can use a list of topic-specific keywords to train the model.

  • Detecting the topics of text fragments

    Efficiently categorize and rout customer inquiries to the corresponding customer service consultant with topic detection. Topic detection scans a piece of text and determines the underlying topic, and then automatically assigns the text to a predefined category.

  • Analyzing natural language

    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.

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