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A label is a structured summary of an intent or concept expressed within a verbatim. A verbatim is typically summarised by multiple labels - i.e. a label isn't a mutually exclusive classification of the verbatim. 

Labels are initially created by users, and then can either continue to be applied (or ‘pinned’) by users or automatically predicted by Re:infer.


As an example, in a dataset monitoring the customer experience we might create a label called ‘Incorrect Invoice Notification’, which describes when a customer is informing the business that they’ve received what they believe is an incorrect invoice. 

Labels can be organised in a hierarchical structure to help you organise and train new concepts more quickly. In both the examples above hierarchical labels have been used, e.g. ‘CS > Agents’ and ‘Margin Call > Full Agreement’.


See the below screenshots for different examples of verbatims with different applied labels. 



An email in the operations team of a financial services company


Labels are shaded by the confidence that Re:infer has in the predicted labels. The more opaque the label, the higher Re:infer’s confidence is that the label applies.


For datasets with sentiment analysis enabled, every label has an associated positive or negative sentiment indicated by a green or red colour (see below).


A screenshot of a cell phone

Description automatically generated


A response to an NPS survey. Two labels have been applied to this Verbatim one with positive sentiment the other with negative sentiment



Label creation and editing actions are primarily performed in the Explore and Discover pages. 

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