Increase model accuracy by correcting the sentiment value, intent, category, or entity classification of the analyzed text. After collecting the corrected records, retrain the corresponding model to classify similar records more accurately in future analyses.
Perform the following tasks to learn about providing feedback to text analytics models:
- Providing feedback through an API
Create a feedback loop for your models by using the pxCaptureTAFeedback activity.
- Feeding the feedback data to text analytics models
You can update a text analytics model by extending the training data with the recorded feedback.
- Updating models by uploading the feedback data
You can update a model by importing feedback data from an .xls file.
- Best practices for providing feedback for text categorization models
When working with text categorization models (sentiment, topic, and intent detection), you must select a portion of the text and provide your feedback.
- Best practices for providing feedback for text extraction models
For text extraction models, you can provide feedback on named entity recognition for regular text extraction models. You can also provide feedback for the email parser rule that distinguishes between various email components.