Email channel NLP model

The natural language processing (NLP) model for the configured Email channel is saved as a text analyzer rule. Pega Email Bot uses this rule to analyze the text of the received email for sentiment analysis, text (topic) classification, intent analysis, and entity extraction.

Each record item in the list represents an email conversation with a customer that is saved by the Email channel. Each record item has an assigned topic that is used for text analysis. Only record items are saved and appear on the Training data tab when a user creates a case that is not suggested or sends a reply that is not suggested.

By updating the NLP model for the configured channel, you improve the machine learning capability for the channel instance. You send feedback from the Training data tab about the outcome of text analysis. When you edit a record item and match it with the expected outcomes, the subsequent text analysis results have a better confidence score, and the sentiment analysis, text (topic) classification, intent analysis, and entity extraction are more accurate.

To get the training data added to the NLP model for the channel, a designer or a data scientist must open Prediction Studio ( pyPredictionStudio ), select the taxonomy for the channel, and build the model with the updated training data. For more information, switch your workspace to Prediction Studio and access the Prediction Studio help system.