- 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
- 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
Communications Mining User Guide
Enabling sentiment on a dataset
User permissions required: ‘Datasets admin’.
Before you start training, you’ll need to choose whether to enable sentiment analysis when creating your dataset. This is an important decision as it will effect how you annotate each message, as well as the output of the platform's 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 (there is no neutral sentiment).
This 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 (i.e. are people happy with X or dissatisfied about Y).
When would you enable sentiment analysis?
Sentiment analysis is most useful for more emotive communications data, such as customer (or employee) feedback reviews and surveys, or support tickets and chats, when you’re trying to gain a sense of customer (or employee) satisfaction (or dissatisfaction) regarding various topics.
Sentiment analysis is not typically recommended for communications data that is generally neutral in tone, such as shared mailboxes for BAU teams interacting with each other or external counterparts (though there can be exceptions). In these kinds of data sources, sentiment is usually only expressed occasionally, but you would need to assign positive or negative sentiment to each label if it was enabled.
For more neutral datasets, it is typically easier to capture sentiment with certain inherently positive or negative labels, such as ‘Frustration’ or ‘Chaser’ as there are far fewer cases where sentiment is explicit.
How to enable sentiment is covered in the previous article here.