The text categorization feature assigns the analyzed text to one or more predefined categories. Pega Platform provides you with three types of text categorization, depending on what you want to detect:

Topic detection

Topic detection determines the underlying topic of a single piece of text or an entire document to efficiently route an incoming customer query to the right agent.

For example, in a chat window on the Emu Airlines website, a customer writes I want to book a ticket from London to Tokyo. Topic detection automatically assigns the message to the Booking a flight category based on similar customer messages and the book and ticket keywords. The company application routes all messages from the Booking a flight category to the corresponding customer service team. As a result, Emu Airlines reduces the response time to customer queries and improves the quality of their customer service.

Topic detection supports both machine learning and keyword-based categorization.

For more information, see Detecting the topics of text fragments.

Intent detection

Intent detection analyzes a unit of text, for example, a comment on your company social media profile, to determine the intent of the author. For example, intent detection classifies the sentence How do I create an account at uPlusTelco? as an inquiry.

Intent detection supports machine learning categorization.

For more information, see Recognizing user intent.

Sentiment detection

Sentiment detection recognizes the feelings (attitudes, emotions, opinions) that characterize a unit of text and then assigns the analyzed text to one of the following sentiment categories: positive, neutral, or negative. For example, the sentence I am happy with uPlusTelco's customer service. is determined as positive.

Sentiment detection supports machine learning categorization.

For more information, see Determining the emotional tone of text.