Find Insights: Tag

Tagging allows you to quickly, simply, and easily understand what's happening in your data at a granular level.



Your iterative process to identify key themes in your unstructured data

What Are Tags?

Tags (also known as labels or code-frames) are simply keywords or keyphrases strongly related to an item (e.g. a comment, a description, etc.).

Take for example, the statement:

I travelled to Australia last summer and enjoyed the amazing nature.

Good tags could be: Australia, Travel, and Nature

Applying tags to items in a dataset is referred to as tagging.

What comes with tagging?

  • Group items so that those in with the same tag have meaningful similarities (i.e. specific features or properties).
  • One-to-many assignment. Meaning, that an individual data point (e.g. response, document, comment) can be assigned to one or more tags. (If you would like a one to one relationship, see 'Cluster').
  • Informed decision-making through the identification of different patterns. It is is a great tool to unravel hidden patterns in the data.
  • Uncover unexpected and emerging themes without bias, by viewing the data without the need for an upfront, fixed taxonomy
  • Drill down to identify the detail behind each tag, including what's driving each group, instantly

Tag Editor in Relevance AI

Coming soon!

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How to tag data in Relevance AI

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