Under various circumstances, data can be grouped into different categories. Categories might:
- Already exist in the dataset (e.g. States, NSP categories, department names, age groups, gender, etc.)
- Be an outcome of analysis such as clusters AI Text Clustering, codes AI Tagging or automatically extracted themes in Interview AI
On this page, we are learning the basic steps for setting up a category view on Explore dashboard. We will use AI clustering results but any categorical field can be used.
What is a categorical field
Categorical fields contain a finite list of values that are repeated among the items in a dataset. For instance, in a dataset of 1000 customer queries, if there is a field identifying the month queries come in, the values in this field are 12 month names repeated among the 1000 query documents; therefore, month is a categorical value. Same applies to fields such as gender, NPS category, names of departments within an organization, education, country of origin, etc.
To add a category view to an Explorer dashboard click on the
+ sign on the top bar, and select "Category View" from the drop-down menu.
Type your desired name for the view, select a color for the tab/page (optional) and hit add page.
On the new page that automatically pops open, click on the "Select Category" button. Next, you can choose any of the categorical fields existing in your dataset. These fields can be preexisting ones (e.g. gender, NPS category) or results of applied analysis (e.g. AI Text Clustering, AI Tagging, Extract Sentiment).
For using analysis results, select the desired field from the menu. The picture below shows a sample choosing clustering results.
When done, you can see a new tab/page is added to the top bar.
Components of category view tabs are fully explained on the next page.
Updated 4 months ago