How To Tag Data In Relevance AI
How To Tag Responses in Relevance AI
After data is uploaded to the Relevance AI platform, tagging can be done through a variety of options. The most common tagging workflow is AI-Tagging.
This workflow allows you to
- build up your taxonomy (i.e. candidate tags) and apply it to your dataset. To form a taxonomy, you can use your own code frames/ tags or automatically extract keywords/key phrases (or code frames) using AI models on your data or clustering results. You are able to review and modify AI-suggested tag candidates before applying the final list to your dataset.
Review your taxonomy
It is highly recommended to review the taxonomy before starting to tag the dataset. Good taxonomy saves you a lot of time under review and refine step.
- apply your code-frame to your dataset. You are provided with a user-friendly GUI as well, which allows you to refine the tagging results.
Note1: it is highly recommended to include your domain knowledge by modifying the candidate tags before tagging as well as refining the results after AI-Tagging is finalized.
Note2: Tagging algorithms require complex computation that can take long on large datasets. You can start your workflow on the cloud and take care of other tasks while your dataset is being updated with tagging results.
Review: Edit, Update, Merge and Finalize Tags
- Read more about tag review and Editor on the GUI
- There are also separate workflows that allow you to modify your tagging results, such as:
TIPS - What is a good tag?
Is the tag precise?
This means that the tag properly conveys the intended topic and minimises opportunity for ambiguity.
There are several ways that a tag can be not precise. For example: “Pricing/dollars, incl. deals”
Could be split into 2 separate tags: “Pricing” and “Deals”
“Convenience of Site” → “Close Proximity”
"sydney" -> "Sydney" (Capital letters matter!) and similarly "Product Condition" -> "product condition" will help the AI
"Shopping Experience" could instead be broken down into different components that make up the shopping experience such as "checkout experience", "parking lot", "friendly staff".
Is the tag concise?
This means that this is not an incredibly wordy tag.
A tag should not have unnecessary words where possible, as this can dilute meaning.
- For example: “Good Customer Service” → “Customer Service” (unless you are specifically looking for the good component of the word.
- This is important because good customer service will not tag customer service but customer service will tag good customer service).
Related Articles (coming soon)
Editing, Updating and Merging Tags, Categories or Themes
Tips To Improve Tag Accuracy and Granularity
What is the minimum number of records to get meaningful results?
Updated 29 days ago