Setting up a machine-learning topic detection model
Start the build process of a keyword-based topic detection model by specifying the model name, language, and corresponding ruleset.
In the navigation pane of Prediction Studio, click Models.
In the header of the Models work area, click .
In the New text categorization model window, set up the new model:
In the Name field, enter a name for the topic detection model.
In the Language list, select a language for the model to use.For more information, see Language support for NLP.
In the What do you want to detect? section, click Topics, and then select the Use machine learning check box.
In the Save model section, specify the class in which you want to save the model, and then specify its ruleset or branch.
Open the model creation wizard by clicking Start.
- 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.