Creating text classification analysis models

Text classification models classify text into one of several categories. You can use this type of analysis in customer service to automatically classify customer queries into categories, thus increasing the response time. By classifying text, you can also route the query directly to the right agent.

You can use Pega Platform to create text classification models by using machine learning: Maximum Entropy, Naive Bayes or Support Vector Machine.

Prerequisites

To build machine learning models, you must access the analytics center. This can be done by launching the pyDecisionAnalytics portal. Add this portal to the list of portals in your access group. For more information see, Access Group form - Completing the Definition tab.

1. In Designer Studio, click Launch > Analytics Center.
2. In the Analytics Center work area, click Create and then click Text classification.
3. In the Create Text Classification window, perform the following actions:

   1. Enter the name of the classification model.

   2. Select the language of the model.

   3. In the Creation section, select the Build new model check box.

   4. Click Start.

4. Perform the following actions:

   1. Prepare data.

   2. Upload data for training and testing.

   3. Define the training and testing sample.

   4. Create the model.

   5. Review the model.

   6. Export the model.

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