- Getting started
- Balance
- Clusters
- Concept drift
- Coverage
- Datasets
- General fields (previously entities)
- Labels (predictions, confidence levels, hierarchy, etc.)
- Models
- Streams
- Model Rating
- Projects
- Precision
- Recall
- Reviewed and unreviewed messages
- Sources
- Taxonomies
- Training
- True and false positive and negative predictions
- Validation
- Messages
- Administration
- Manage sources and datasets
- Understanding the data structure and permissions
- Create a data source in the GUI
- Uploading a CSV file into a source
- Create a new dataset
- Multilingual sources and datasets
- Enabling sentiment on a dataset
- Amend a dataset's settings
- Delete messages via the UI
- Delete a dataset
- Delete a source
- Export a dataset
- Using Exchange Integrations
- Preparing data for .CSV upload
- Model training and maintenance
- Understanding labels, general fields and metadata
- Label hierarchy and best practice
- Defining your taxonomy objectives
- Analytics vs. automation use cases
- Turning your objectives into labels
- Building your taxonomy structure
- Taxonomy design best practice
- Importing your taxonomy
- Overview of the model training process
- Generative Annotation (NEW)
- Understanding the status of your dataset
- Model training and annotating best practice
- Training with label sentiment analysis enabled
- Train
- Introduction to Refine
- Precision and recall explained
- Precision and recall
- How does Validation work?
- Understanding and improving model performance
- Why might a label have low average precision?
- Training using Check label and Missed label
- Training using Teach label (Refine)
- Training using Search (Refine)
- Understanding and increasing coverage
- Improving Balance and using Rebalance
- When to stop training your model
- Using general fields
- Applying labels
- Reviewing messages
- Searching for messages
- Label editing
- Generative extraction
- Using analytics and monitoring
- Automations and Communications Mining
- Licensing information
- FAQs and more
Reviewing messages
User permissions required: ‘View Sources’ AND ‘Review and annotate’.
Overview
Reviewing unreviewed messages and accepting or rejecting the platform's predicted labels and general fields further trains the model and its accuracy.
You can review unreviewed messages in most of the training modes in Explore and in Discover:
- Cluster (Discover)
- Search (Discover & Explore)
- Recent (Explore)
- Shuffle mode (Explore)
- Label mode (Explore)
- Teach (Explore)
- Low Confidence (Explore)
Make sure to apply all of the relevant labels in your taxonomy to each message. When you review a message, not only do you teach the model which labels apply, but also which labels don’t. If you don’t apply all relevant labels, you send a negative training signal to the model, which will affect its performance.
The opacity of a label indicates the confidence of the platform's prediction of that label, with higher opacity indicating higher confidence.
Hovering your cursor over the label opens a modal showing the confidence with which the model has predicted the label and, if sentiment analysis is enabled, the net sentiment.
- Clicking on the label, or the sentiment indicator (if sentiment analysis is enabled) pins the label to the message, i.e. it confirms the model’s prediction of that label
- If you want to change the sentiment of the predicted label, click the face image that appears when you hover over the message
- If the prediction is wrong, add the correct one - this effectively dismisses the incorrect predictions
Hovering your cursor over the general field opens a modal showing the confidence with which the model has predicted the general field.
Accept/reject a general field
- Clicking 'Confirm' on the general field (or clicking the hotkey - which is '1' to confirm a general field), pins the general field to the message, i.e. it confirms the model’s prediction of that label
- Clicking the 'Dismiss' on the general field (or clicking the hotkey - which is '2' to dismiss a general field), tells the platform that the general field predicted is incorrect
- Clicking the change general field button allows us to assign a different general field, if the general field predicted is incorrect
- In the example above, clicking this button will display the other general fields in our dataset that we can assign
- In this case, we can change the general field type from 'Cancellation Date' to 'Policy Start' on this dropdown, which will assign this general field
