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Configuring sentiment score range


You can define a sentiment score range to specify the type of sentiment feedback that you receive: positive, negative, or neutral.

You define neutral sentiment within the available score range ( -1 to 1 ). Sentiments with a higher score than the neutral range are positive and with the lower score are negative. This setting is helpful when you need to comply with your business requirements and precisely adjust the sentiment ranges. For example, narrowing the negative score range helps to identify the most critical text-based content such as news feeds, emails, and postings on social media streams.
  1. In the Records panel, click Decision Text Analyzer .

  2. Click the Advanced tab.

  3. In the Sentiment settings section, enter the minimum and maximum score to define the score range for the neutral sentiment, or keep the default values -0.25 and 0.25.

    Do not define the neutral sentiment score range as -1 to 0 or 0 to 1 because these ranges interfere with sentiment analysis of input texts. The first score range excludes negative sentiment from sentiment analysis; the second score range excludes positive sentiment.

To understand this configuration, analyze the following text with the default sentiment score values: Your company provides very good service. Still, the prices are too high. I have a neutral opinion about you.

The first sentence has positive sentiment, the second negative, the last one neutral. The overall sentiment for the whole text is neutral because the sentiment score equals 0.03, which belongs to the neutral sentiment score range ( -0.25 to 0.25 ).

  • Sentiment analysis

    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.

  • Configuring advanced text analytics settings

    Configure language detection settings, enable spell checking, and control how the text is categorized, based on various criteria.

  • Configuring language detection preferences

    You can control how a text analyzer detects languages in the analyzed document. For example, you can enable a fallback language in case your text analyzer does not detect the language when analyzing content that is written in multiple languages.

  • Configuring spelling checker settings

    By using the spelling checker, you can categorize the text with a greater confidence score, making the analysis more accurate and reliable.

  • Configuring categorization settings

    Categorization settings give you control over how the text is categorized, depending on the selected level of classification granularity. You can adjust text categorization according to your business needs, for example, change the analysis granularity to document level if you analyze short tweets. The Topic settings section is available only when the categorization analysis is enabled on the Select Analysis tab.

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