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Importing a taxonomy for keyword-based topic detection

Updated on May 17, 2024

After you create a topic detection model, import a taxonomy that contains defined topics and keywords for topic detection. Based on the keywords, topic detection assigns topics to the analyzed piece of text.

Before you begin:
  1. Create a .csv, .xls, or .xlsx file with defined topics and corresponding keywords. For more information, see Requirements and best practices for creating a taxonomy for rule-based classification analysis.
  2. Create a keyword-based topic detection model by specifying the model name, language, and corresponding ruleset. For more information, see Setting up a keyword-based topic detection model.
  1. In the Taxonomy workspace, click ActionsImport.
  2. In the Import taxonomy dialog box, click Choose file, and then select the .csv or .xlsx taxonomy file.
  3. Click Import.
  4. To detect child topics only when the corresponding parent topic is detected, for the parent topic, select the Match child topics only if the current topic matches check box.
  5. Optional: To test your taxonomy, select ActionsTest.
    Tip: Always test your taxonomy on a number of text samples to determine whether it accurately assigns topics. Depending on the results, you might refine your taxonomy, for example, by increasing the number of Should words to accommodate additional use cases, or by adding Not words to help differentiate between similar categories.
  6. Save the taxonomy by clicking Save.
    You can use the taxonomy as part of a machine learning topic detection model or directly in Text Analyzers to perform keyword-based topic detection.
Result: Your model is now available in the Models workspace.
  • Previous topic Creating a taxonomy for keyword-based topic detection
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