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Testing Text Analyzer rules


You can test the performance of a Text Analyzer rule after you configured that rule to perform natural language processing tasks that fulfill your business requirements.

You can use real-life data such as Facebook posts, tweets, blog entries, and so on, to check whether your configuration produces expected results. Testing facilitates discovering potential issues with your configuration and fine-tuning the rule by retraining text analytics models, modifying topic detection granularity, changing the neutral sentiment score range, and so on.

  1. In the Records panel, click Decision Text Analyzer .

  2. In the Text Analyzer rule form, click Actions Run to open the Run window.

  3. In the Run window, in the Sample text field, paste the text that you want to analyze.

  4. Click Run.

  5. View the test run results:

    • In the Overall sentiment section, view the aggregated sentiment of the analyzed document, the accuracy score, and the detected language. Each sentiment type is color-coded.

      The following highlight colors are used to identify the sentiment of the text:

      • Green – Positive
      • Gray – Neutral
      • Red – Negative
    • In the Category section, view the categories that were identified in the document. These categories are part of the selected taxonomy. You can also view the sentiment and confidence score for each category.
    • In the Intent section, view the detected intent types and the associated confidence score. There can be multiple intent types detected in the analyzed sample.
    • In the Text extraction section, view the entities that were identified in the document, such as auto tags or keywords. You can also view the summary of the analyzed text and highlight the content that was extracted to form the summary in the original text.
    • In the Topics section, view the categories that the text analyzer extracted from the document.

  • 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.

  • Intent analysis

    Through intent analysis, you can determine the expressed intent of your customers or product reviewers.

  • Topic detection

    This type of text analysis determines the topics to which a text unit should be assigned. In Pega Platform, topic detection is achieved by means of machine learning-based and keyword-based models. By categorizing text into topics, you can make it easier to manage and sort, for example, you can group related queries in customer support.

  • Text extraction analysis

    Text extraction analysis is the process of extracting named entities from unstructured text such as press articles, Facebook posts, or tweets, and categorizing them. Typically, a named entity is a proper noun that falls into a commonly understood category such as a person, organization, or location. An entity can also be a Social Security number, email address, postal code, and so on.

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