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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 Creating sentiment analysis models.
- 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).
- Optional: 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.
- Optional: 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.
- Click Save.
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