Communications Mining
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- Getting Started
- 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
- Export a dataset
- Using Exchange Integrations
- Preparing Data for .CSV Upload
- Model Training and Maintenance
- Understanding labels, entities 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 labelling 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
- Applying labels
- Reviewing messages
- Searching for messages
- Label editing
- Using Analytics & Monitoring
- Automations and Communications Mining
- FAQs and More
Communications Mining User Guide
Last updated Apr 18, 2024
Searching for messages
User permissions required: ‘View Sources’ AND ‘View Labels’.
You can use search in either Explore or Discover (switching from cluster mode via the dropdown menu) to search for messages containing specific terms or phrases. The platform highlights occurrences of your search terms within the messages (as shown below).
Search in Explore vs. Discover
- The key difference between searching in Discover and Explore, are that in Discover you can label search results in bulk (just like labelling clusters), whilst in Explore you label them individually
- Unlike Discover, however, Explore returns an approximate total number of messages that match your search term (as shown below) - this can be very useful when trying to gauge how many examples there may be in your dataset before creating a label, if there are terms very closely linked to the label concept
Example search query in Explore
Example search query in Discover
