Managing text analytics models
Data scientists can perform various housekeeping activities for sentiment and text classification models in the Predictions work area in Prediction Studio. The range of available activities depends on whether the model has been built (the displayed model status is Completed) or is incomplete (the displayed model status is In build).
- Managing incomplete text analytics models
If a text analytics model build process does not finish or was interrupted in any way, the model is displayed in the Predictions work area with the In build status. You can resume building an incomplete model or remove the model from the work area.
- Managing complete text analytics models
Quickly and conveniently manage multiple models to accommodate them to ever-changing business requirements, through a wide range of the available types of actions requirements. You can test, update, or delete any completed categorization or text extraction model. You can also add a language to the model or save the model as a different rule instance.
- Analyzing natural language
Effortlessly analyze and extract meaningful information from large volumes of text with the use of text analytics. Based on your findings, you can further improve business performance and customer experience.
- Creating machine-learning topic detection models
Efficiently connect your customers with the right consultant by providing training data to a topic detection model.
- Determining the emotional tone of text
Sentiment analysis determines whether the opinion that the writer expressed in a piece of text is positive, neutral, or negative. Knowledge about customers' sentiments can be very important because customers often share their opinions, reactions, and attitudes toward products and services in social media or communicate directly through chat channels.