Configuring topic settings in text predictions
Use topic settings to control how text is categorized, depending on the selected level of classification granularity. You can adjust text categorization according to your business needs, for example, to change the granularity of the analysis to document level if you analyze short chat messages.
- Open the text prediction:
- In the navigation pane of App Studio, click Channels.
- In the Current channel interfaces section, click the icon that represents a channel for which you want to configure the text prediction.
- On the channel configuration page, click the Behavior tab, and then click Open text prediction.
- In the Prediction workspace, click the Settings tab, and scroll down to the Topic settings section.
- In the Pega NLP section, configure granularity for Pega
NLP models:
Choices Actions Define sentence-level granularity In the Pega NLP section, select Sentence level.
Sentence-level granularity is optimal for high-precision analysis when you want to identify the top topic for each sentence separately. Use this feature to analyze large units of text, for example, emails or blog entries.
Define document-level granularity - In the Pega NLP section, select Document level, and then click Configure.
- In the Document level granularity
window, select one of the following options:
- To detect only a specified number of topics that received the highest confidence score, select Select an amount of top topics, and then enter the appropriate amount.
- To limit the number of detected topics to only those above a specific confidence score threshold, select Select topics above a confidence score threshold, and then enter the appropriate threshold.
Document-level granularity is useful when you want to categorize the text as a whole, with no further breakdown. Use this feature to analyze smaller units of text, for example, chat messages.
- Optional: Enable the option to fall back to rule-based topic detection if the specified confidence threshold is not met.
- Click Submit.
Configuring the confidence score threshold and fallback setting 
- In the External models section, configure granularity for
external models:
- To detect only a specific number of topics that received the highest confidence score, select Select an amount of top topics, and then enter the number of topics to detect.
- To limit the number of detected topics to only those above a specific confidence score threshold, select Select topics above a confidence score threshold, and then enter a threshold value.
- In the Taxonomy section, in the Parent
topic field, enter a parent topic for all the topics that you
add to this prediction.By default, the parent topic is set to action.
- Click Save.
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