Text analytics features in Pega Platform
The Pega Platform provides a collection of features that you can use to process and structure text data from social media platforms:
- Sentiment analysis – Detects and analyzes the feelings (attitudes, emotions, opinions) that characterize a unit of text, for example, to find out whether a product review was positive or negative.
- Topic detection – Assigns one or more classes or categories to a text sample to make it easier to manage and sort.
- Intent analysis – Determines whether the content that you analyzed in your application was produced with any underlying intention, for example, whether a person is likely to buy your product or wants to complain.
- Text extraction – Extracts named entities from text data and assigns the detected entities to predefined categories such as names of organizations, locations, people, quantities, or values.
Depending on the language of the analyzed content, various Pega Platform features help you to obtain accurate analysis results.
See the following table for the full list of supported languages and the corresponding features:
|Language||Sentiment||Topic detection||Text extraction||Continuous learning|
In addition, Pega Platform can detect the following languages: Arabic, Basque, Belarusian, Bulgarian, Catalan, Croatian, Czech, Danish, Esperanto, Finnish, Hindi, Hungarian, Icelandic, Indonesian, Japanese, Korean, Lithuanian, Mandarin, Persian, Polish, Romanian, Russian, Serbian, Swedish, Turkish, Ukrainian, and Vietnamese.
You can process other languages by integrating your application with Google Dialogflow. For more information, see Google Dialogflow Text Analyzer.