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Sentiment analysis

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Sentiment analysis determines whether the analyzed text expresses a negative, positive, or neutral opinion. By analyzing the content of a text sample, it is possible to estimate the emotional state of the writer of the text and the effect that the writer wants to have on the readers. Sentiment analysis in Pega Platform combines the lexicon-based and machine learning-based approaches to predict the polarity of the analyzed text.

Lexicons

In Pega Platform, lexicons are lists of features that provide sentiment values for words, multiple sentiments within a phrase (for example, ridiculously awesome), negation words (for example, not and no), and stop words (for example, because, such, have). Use lexicons as semantic features for machine learning. Lexicons are defined for each supported language and stored as About Decision Data rules.

Sentiment models

Text analyzers can contain algorithms that act on words, phrases, sentences, or the whole text. Pega Platform uses a maximum entropy algorithm to train sentiment analysis models. When the training is completed, you can upload the model as part of a text analyzer to perform sentiment analysis in your application to analyze the voice of customer materials, such as reviews, Facebook posts, tweets, emails, and so on. You can train custom sentiment analysis models in Prediction Studio.

Sentiment score

Each sentence that undergoes sentiment analysis is assigned a sentiment score between -1 and 1. The individual scores of all sentences are used to calculate the overall sentiment of the text unit. You can Configuring sentiment score range for the negative sentiment to decide how your text analyzer detects sentiment.

  • Configuring sentiment analysis

    Select the sentiment model and the lexicon to apply on the data that you want to analyze.

  • 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.

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