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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.

The topic detection model then scans the customer message in search of the specified keywords, and uses the results to assign the message to a corresponding topic, for example Action User Support .

Keyword-based topic detection acts as a substitute for, or supplement to, machine learning topic detection. For more information about the differences between topic detection model types, see Comparing keyword-based and machine-learning topic detection.

To create a keyword-based topic detection model, perform the following procedures:

  • Setting up a keyword-based topic detection model

    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.

  • Creating a taxonomy for keyword-based topic detection

    After you create a topic detection model, define the topics that you want to detect in a piece of text. For each topic, add a list of keywords that define the topic. Based on these keywords, topic detection then assigns topics to an analyzed piece of text.

  • Importing a taxonomy for keyword-based topic detection

    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.

  • 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.

  • Creating machine-learning topic detection models

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

  • 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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