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Preparing data for intent detection


In the Lexicon selection step, provide a sentiment lexicon and a list of intent types, together with words or phrases that are specific to each intent type that yo want to detect.

  1. In the Lexicon drop-down list, select a sentiment lexicon that you want to use in the model building process.

    A sentiment lexicon provides the list of features that are used in sentiment analysis and intent detection. You can use the default lexicon based on the pySentimentLexicon rule provided by Pega. For more information, see Sentiment lexicons.
  2. Define the intent types that you want to detect by performing the following actions:

    1. In the Intent config selection section, click Add item.

    2. In the Intent field, enter the name of the intent type, for example, Purchase.

    3. In the Action field, enter verbs or verb phrases that describe the user ideas or actions with regard to the intent type, for example, buy, purchase, want to acquire, intend to order, need to purchase, and so on.

    4. In the Subject field, enter any domain-specific words or phrases (for example, nouns or noun phrases) that relate to the intent type that you specified, for example, laptop, new phone, service, internet plan, and so on.

  3. Click Next.

  • 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.

  • Recognizing user intent

    Create intent analysis models to enable your application to detect the ideas that users express through written communication. For example, you can use an intent model when you want your chatbot to understand and respond when someone asks for help.

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