Reviewing the topic detection model

Review the created model by analyzing the results of testing against the provided training data.

1. In the Model analysis Analysis wizard step, view the outcome of the classification analysis of the testing sample by clicking Download report.

   You can view the following types of classification measures:

   • Topic detection analysis results.
   • Manual (actual) and machine (predicted) outcome comparison.
   • True positive, precision, recall, and F-score measures.
2. Optional:

   For detailed information about the achieved accuracy, click the Expand icon next to each model that you created.

   You can view the following data:

   • On the Class summary tab, view the predicted (manual) and actual (machine) outcome comparison of the assigned classification values. Additionally, you can view true positive, precision, recall, and F-score measures.
   • On the Test results tab, view the classification analysis of the records in the testing sample. You can view the actual (machine), and predicted (manual) outcomes, as well as whether the actual and manual outcomes match.

3. Click Next.

• Creating machine-learning topic detection models

  Efficiently connect your customers with the right consultant by providing training data to a topic detection 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.

• Text analytics accuracy measures

  Models predict an outcome, which might or might not match the actual outcome. The following measures are used to examine the performance of text analytics models. When you create a sentiment or classification model, you can analyze the results by using the performance measures that are described below.