{"id":1114134,"date":"2026-09-23T22:30:00","date_gmt":"2026-09-24T05:30:00","guid":{"rendered":"https:\/\/www.questionpro.com\/blog\/?p=1114134"},"modified":"2026-09-21T06:08:02","modified_gmt":"2026-09-21T13:08:02","slug":"heres-how-emerging-themes-in-textai-can-help-you-survive-wave-seven-with-a-wave-one-codebook","status":"publish","type":"post","link":"https:\/\/www.questionpro.com\/blog\/heres-how-emerging-themes-in-textai-can-help-you-survive-wave-seven-with-a-wave-one-codebook\/","title":{"rendered":"Here’s how emerging themes in TextAI can help you survive wave seven with a wave one codebook"},"content":{"rendered":"\n

Your codebook was built for wave one. Your respondents have moved on. Here’s how to review what’s new. Modern text analysis for market research requires a system that automatically surfaces emerging themes so your codebook evolves alongside consumer trends.<\/em><\/p>\n\n\n\n\n\n

Wave seven, and the codebook is still from wave one<\/strong><\/h2>\n\n\n\n

Say you run a brand tracker for an FMCG snack brand in the US. Every quarter, one open-ended question asks what comes to mind when people think of the brand. Two years ago, you built a codebook for the answers: flavor, packaging, availability, and value.<\/p>\n\n\n\n

Then a competitor launches a high-protein line. In wave seven, respondents start writing about protein and comparing the brand to the newcomer. Some comments get squeezed under flavor. Others land in Outlier, the category for responses that match no topic.<\/p>\n\n\n\n

Nobody flags it. The readout goes out with the same four themes. The new conversation sits unread until a stakeholder asks why the deck never mentioned protein.<\/p>\n\n\n\n

This is where emerging themes in TextAI could have saved the day.<\/p>\n\n\n\n

Learn More:<\/strong> Emerging themes in TextAI help documentation<\/a><\/p>\n\n\n\n

How does TextAI analyze open-ended responses in QuestionPro BI?<\/strong><\/h2>\n\n\n\n

TextAI is the text analysis tool for market research inside QuestionPro BI<\/a>. It reads open-ended responses and organizes them into themes and sub-themes. Responses that match no topic go under Outlier.<\/p>\n\n\n\n

You choose the structure. Upload your own codebook, or generate one with QuestionPro AI. Then refine the results in Theme configuration. Create themes, merge similar ones, assign responses by hand, and recode.<\/p>\n\n\n\n

For setup and the wider workflow, see the advanced text analysis<\/a> and TextAI qualitative analysis<\/a> posts.<\/p>\n\n\n\n

A shared codebook keeps studies and analysts consistent. That’s its strength, and also why it can lag when the conversation changes.<\/p>\n\n\n\n

What are emerging themes in TextAI?<\/strong><\/h2>\n\n\n\n

Emerging themes are candidate themes and sub-themes for you to review. They appear in Theme configuration, labeled Pending approval. Each candidate comes with a sub-theme description and a response percentage.<\/p>\n\n\n\n

Select a pending sub-theme and the raw-response panel filters to the comments behind it. When you’re ready, Approve is waiting in the selection bar.<\/p>\n\n\n\n

Candidates are suggestions, not verdicts. They give you a review step where you’d otherwise be reading raw comments and guessing. Want the launch details? Read the emerging themes announcement<\/a>.<\/p>\n\n\n\n

How to use emerging themes in your market research<\/strong><\/h2>\n\n\n\n

Back to wave seven. Add the new responses to the source behind the dashboard, then click Process now. When processing finishes, open Theme configuration. If a candidate turns up, it’s labeled Pending approval. Say it’s a sub-theme about protein.<\/p>\n\n\n\n

Select it. The raw-response panel shows the comments behind it. Read them next to the description and the response percentage. Then ask three questions. Do these comments describe one coherent topic? Is the name accurate? Is it distinct from flavor and your other themes?<\/p>\n\n\n\n

If it holds up, click Approve. If it doesn’t, leave it be. Either way, read through Outlier too. Not every unfamiliar comment becomes a candidate, so the feedback outside your named themes still deserves a look.<\/p>\n\n\n\n

Now the readout has evidence. Instead of guessing whether protein matters, you’re reading what respondents actually wrote. You decide before the deck goes out, not after a stakeholder asks.<\/p>\n\n\n\n

The advantages of emerging themes for insights teams<\/strong><\/h2>\n\n\n\n

Here’s what changes when new topics arrive with their evidence attached.<\/p>\n\n\n\n