NLP model for a Facebook channel

The NLP model for the configured Facebook channel is saved as a text analyzer rule. The Pega Intelligent Virtual Assistant for Facebook uses this rule to analyze any text of the chat conversation for sentiment analysis, text (category) classification, intent analysis, and entity extraction.

Each record item in the list represents a received chat conversation text from a customer that is saved by the channel. Each record item has an assigned category that is used for text analysis. Only record items that generate no match or multiple matches text analyzer warnings are saved and appear in the Training data tab.

By updating the NLP model for the configured channel, you support the machine learning capability for the channel instance. You send feedback in the Training data tab on 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 (category) 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 the Analytics Center portal ( pyDecisionAnalytics ), select the taxonomy for the channel, and build the model with the updated training data. For more information, see Analytics Center portal .