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Uploading data for training and testing of the topic detection model


Upload sample records to train the model and to test whether the model assigns the topics correctly.

Prepare a .csv, .xls, or .xlsx file with training and testing data, for example, previous customer messages that have assigned categories.

To view the structure required for the training and testing data as well as the sample records, in the Source selection wizard step, click Download template.
  1. In the Source selection wizard step, click Choose file.

  2. Select a .csv, .xls, or .xlsx file with sample records for training and testing the model.

    Ensure that the file contains sample records with assigned categories.
  3. Optional:

    To enable spellchecking, perform the following actions:

    1. Select the Use spell checking check box.

    2. To increase the accuracy of the model by correcting any spelling errors, expand the Select spell checker list, and then select a Spelling Checker Decision Data rule, if available.

    Enabling spellchecking can significantly increase the model training time, depending on the size of the training sample. Spellchecking also has an impact on real-time performance of the model.
  4. Click Next.

Split the uploaded data into a set for training the model and a set for testing the model accuracy. For more information, see Defining the training and testing samples for topic detection.

  • 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.

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