User permissions required: ‘Modify datasets’ to enable Tone analysis
What is 'Tone'?
Tone analysis is the process of understanding and identifying the emotional tone or attitude expressed in a verbatim.
The primary purpose of tone analysis is to gain insights into the emotional state of the writer to understand how they're feeling about a specific subject or topic.
Leveraging the platform's tone analysis capabilities allows users to be proactive in customer experience management, and can help users anticipate and address customer needs based on the emotions expressed within their communications.
When should we use tone vs. label sentiment?
Label sentiment is typically only appropriate for customer feedback related datasets. This is because they contain many more identifiable expressions of sentiment than other datasets, which tend to be much more neutral by nature.
For all other use cases (e.g. email inbox analysis and automation), tone should be used because the emotional tone expressed in a verbatim may not simply be a positive vs. a negative sentiment. It could be a complex spectrum of emotions, better represented by a scoring system. The majority of communications in these datasets are also neutral in tone, which is not captured by label sentiment analysis.
How does it work?
Communications Mining's tone model is trained to look for specific expressions of sentiment, both positive and negative, and aggregates them up into an overall 'tone score' between -10 and 10.
This score then becomes a filterable / queryable property for each message, and can be aggregated up at different levels in Reports.
Additionally, the tone score is used as a contributing factor in generating Quality of Service (QoS) scores.
How do I enable it?
Enable Tone analysis in the dataset's settings page. This can be enabled at any time.
Tone Analysis Toggle
What does it look like? 
Example verbatim with Tone enabled
Next: Quality of Service