- Introduction
- Setting up your account
- Balance
- Clusters
- Concept drift
- Coverage
- Datasets
- General fields
- Labels (predictions, confidence levels, label hierarchy, and label sentiment)
- Models
- Streams
- Model Rating
- Projects
- Precision
- Recall
- Annotated and unannotated messages
- Extraction Fields
- Sources
- Taxonomies
- Training
- True and false positive and negative predictions
- Validation
- Messages
- Access Control and Administration
- Manage sources and datasets
- Understanding the data structure and permissions
- Creating or deleting a data source in the GUI
- Uploading a CSV file into a source
- Preparing data for .CSV upload
- Creating a dataset
- Multilingual sources and datasets
- Enabling sentiment on a dataset
- Amending dataset settings
- Deleting a message
- Deleting a dataset
- Exporting a dataset
- Using Exchange integrations
- Model training and maintenance
- Understanding labels, general fields, and metadata
- Label hierarchy and best practices
- Comparing analytics and automation use cases
- Turning your objectives into labels
- Overview of the model training process
- Generative Annotation
- Dastaset status
- Model training and annotating best practice
- Training with label sentiment analysis enabled
- Training chat and calls data
- Understanding data requirements
- Train
- Introduction to Refine
- Precision and recall explained
- Precision and Recall
- How validation works
- Understanding and improving model performance
- Reasons for label 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™
- Developer
- Exchange Integration with Azure service user
- Exchange Integration with Azure Application Authentication
- Exchange Integration with Azure Application Authentication and Graph
- Fetching data for Tableau with Python
- Elasticsearch integration
- Self-hosted Exchange integration
- UiPath® Automation Framework
- UiPath® Marketplace activities
- UiPath® official activities
- How machines learn to understand words: a guide to embeddings in NLP
- Prompt-based learning with Transformers
- Efficient Transformers II: knowledge distillation & fine-tuning
- Efficient Transformers I: attention mechanisms
- Deep hierarchical unsupervised intent modelling: getting value without training data
- Fixing annotating bias with Communications Mining™
- Active learning: better ML models in less time
- It's all in the numbers - assessing model performance with metrics
- Why model validation is important
- Comparing Communications Mining™ and Google AutoML for conversational data intelligence
- Licensing
- FAQs and more
Communications Mining user guide
Overview
The Reports page is where you can view in-platform reporting on your dataset. The reports are all filterable to allow you to check the views that are most important to you.
To access this page, select the Reports tab from the navigation bar. Depending on the type of data, the number of tabs in Reports may vary.
You can toggle between message and thread-level reports, if the data is in thread form, such as call transcripts and email chains. If not, the message filter will be the default.
- Dashboard - Allows you to create custom dashboard views using the data from the other tabs.
- Label Summary - Presents high-level summary statistics for labels.
- Trends - Presents charts for message and label volume and sentiment over a given time period.
- Segments - Presents charts of label volumes versus message metadata fields, such as sender domain.
- Threads - Presents charts of thread volumes and label volumes within a thread. These details are only visible when you apply the Thread filter.
- Comparison - Allows you to compare different groups of data against each other.
- Total number of messages contained in the dataset.
- Net sentiment , if sentiment analysis is enabled.
- Date period for the selected data.
If you apply user property, general field, or label filters, these statistics will update based on the filters and selections that you have made.
If you filter multiple labels, but have no other filters applied, this will show you the total number of messages in the dataset that are likely to have at least one of the selected labels predicted.
