- 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
Enabling sentiment on a dataset
Before you start training, select if you want to enable sentiment analysis when creating your dataset. This option affects how you annotate each message, as well as the output of the platform predictions.
If you choose to enable sentiment analysis, every time you apply a label, you will need to select whether it has positive or negative sentiment, since there is no neutral sentiment.
Enabling sentiment analysis does make the annotating process slightly slower. However, for more emotive communications data, it provides a very useful indication of the overall sentiment of each label. For example, are people happy with X or dissatisfied about Y.
- customer or employee feedback reviews and surveys.
- customer or employee support tickets and chats.
Although there can be exceptions, sentiment analysis is not recommended for communications data that is generally neutral in tone, such as shared mailboxes for BAU teams interacting with each other or external counterparts. In such data sources, sentiment is usually only expressed occasionally. However, if you enabled sentiment analysis, you would need to assign positive or negative sentiment to each label.
For more neutral datasets, it can be easier to capture sentiment with certain inherently positive or negative labels, such as Frustration or Chaser. This is because there are far fewer cases where sentiment is explicit.
For more details on how to enable sentiment, check Creating a dataset.
