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Preparing data for sentiment analysis

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In the Lexicon selection step, select the sentiment lexicon to use for sentiment analysis. Sentiment lexicons contain features that are used to enhance the accuracy of the model.

  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. 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.

  • Determining the emotional tone of text

    Sentiment analysis determines whether the opinion that the writer expressed in a piece of text is positive, neutral, or negative. Knowledge about customers' sentiments can be very important because customers often share their opinions, reactions, and attitudes toward products and services in social media or communicate directly through chat channels.

  • Text analytics accuracy measures

    Models predict an outcome, which might or might not match the actual outcome. The following measures are used to examine the performance of text analytics models. When you create a sentiment or classification model, you can analyze the results by using the performance measures that are described below.

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