- 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)
- Dastaset status
- 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
- Generative extraction
- Using analytics and monitoring
- Automations and Communications Mining
- Licensing information
- FAQs and more
Messages
verbatim(s) changed to
messages.
A message is what we call a single unit of free-form text communication; such as an email, a survey response, a note, or a chat or phone call transcript. Messages are grouped together in sources.
Below is an example of how a message is presented in the Explore page of the user interface.
Message metadata
Every message has associated metadata that consists of structured data points that provide additional information about the communication or conversation and its participants.
All messages are required to have an associated timestamp — which typically corresponds to the time at which that message was originally created.
In addition to timestamps, the platform typically stores additional metadata associated with each message. Typical examples of metadata fields are:
- Name and contact details of the message participants
- Sender and receiver domains for emails
- Number of messages in a chat conversation or number of emails in a thread
- Demographical data, such as gender, age, country etc
- A quantitative measure of customer satisfaction with the interaction, such as Net promoter score (NPS), Customer Satisfaction Score (CSAT) etc
- For phone calls, the raw audio used by the platform to transcribe it
- 3rd party IDs when the Messages are imported from another system, for example the email message ID from an Exchange server
Some datasets may contain messages with PII (personally identifiable information) in their metadata (e.g. `userId`), these fields can be marked as sensitive. By marking these fields as sensitive to view this metadata requires enhanced user permissions.
