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About this page
Hi there 👋 This is a guide and collection of best-practice tips & tricks for text analysis in WhyHive.
Our team is always learning new ways to use the app so expect page doc to evolve over time.
If you need help don’t hesitate to email Matt at [email protected]
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Text analysis is a feature for analysing rows of unstructured text using AI.
Common examples of unstructured text include open-ended responses from surveys, product reviews, comments, or support tickets.
WhyHive uses AI to find and quantify common themes in this data. Once the themes are quantified they can be analysed in many of the same ways that close-ended or numeric data can be.
Text analysis in WhyHive involves two steps.
If you’re familiar with research terminology, you can think of Find Themes as the codeframe generation step, and Assign Themes as the coding step.
Custom Instructions let you give the AI context about the job you’re performing or how you want it to run its analysis.
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Note that Custom Instructions apply when finding themes and assigning them.
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