How to Use Bulk Assign 

When working on a project in Caplena, you may need to assign or adjust topics across multiple rows at once. Instead of handling each response individually, you can use the Bulk Assign feature to speed up your workflow.

What is Bulk Assign?

Bulk Assign allows you to select several rows of responses and apply the same topic(s) to them in one action. You can also control whether existing topics are kept or replaced, and whether rows should be marked as reviewed.

This feature is especially useful when you notice a group of similar responses and want to categorize them consistently without repeating the same steps multiple times.

How to Bulk Assign Topics

1. Select the rows you want to modify (tick the boxes on the left side of the response list).

2. Click on Bulk Assign in the action bar.

3. In the Bulk Assign panel:

   • Add topics: Choose one or more topics from your topic list.

   • Topic assign mode:

     • Add → Keeps existing topics and adds the new ones.

     • Replace → Replaces all current topics with the ones you’ve selected.

   • Row review status:

     • Keep current → Leaves review status unchanged.

     • Reviewed → Marks all selected rows as reviewed.

     • Unreviewed → Marks all selected rows as unreviewed.

4. Click Apply to confirm your changes.

Best Practices

• Use “Add” mode when responses already have relevant topics you don’t want to overwrite.

• Use “Replace” mode if you want to completely recategorize rows.

• Mark as reviewed only when you’re confident the assignment is final — this helps keep your workflow tidy and your AI score accurate.

• Filter first: Applying filters (e.g. by Wave, Source, or existing Topic) before using Bulk Assign ensures you’re only updating the right set of rows.

Sentiment and Reviewed/Unreviewed Rows

• All rows with that topic must be either reviewed or unreviewed.

• Use Bulk Assign → Row review status to align them (e.g. set all to unreviewed) and then try disabling sentiment again.

This ensures sentiment is applied consistently across your dataset.