Configuring sentiment analysis
Select the sentiment model and the lexicon to apply on the data that you want to analyze.Determining the attitude of a writer with respect to a topic (for example, the release of your latest product) can help you detect and address any issues or queries that your customers might have. You can use a variety of default models that apply to different business use cases or you can upload a custom model that you created in the Analytics Center. For more information, see Determining the emotional tone of text.
In the navigation panel, click.
Open the Text Analyzer rule that you want to edit.
On the Select Analysis tab, select the Enable sentiment detection check box.
In the Lexicon field, press the Down Arrow key to specify the lexicon that you want to use. You can use the default pySentimentLexicon.Sentiment lexicons contain words and phrases that are associated with a specific type of sentiment (for example, the word good has positive sentiment). Lexicon items are used as semantic features in machine learning.
In the Sentiment model field, press the Down Arrow key to specify the sentiment model that you want to use. You can use the default model pySentimentModels.Sentiment models can determine the sentiment of phrases, sentences, paragraphs, and so on (for example, the phrase This burger isn't bad at all! has positive sentiment).
Configure language detection preferences.Perform this step to analyze multilingual content and configure your application to always detect the content as written in the specified language. For more information, see Configuring language detection preferences.
Determine the type of feedback that you want to detect by adjusting the score range for detecting sentiment.For example, by adjusting the sentiment score range, you can detect only the extremely negative feedback. For more information, see Configuring sentiment score range.
- 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.
- 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.
- Configuring advanced text analytics settings
Configure language detection settings, enable spell checking, and control how the text is categorized, based on various criteria.