Once your data is in Caplena, whether uploaded manually or pulled in via integrations (e.g., Google, Amazon reviews, Qualtrics, API, etc.), you're ready to start your analysis.
This guide walks you through how to set up your project, choose the right structure, and get the most out of AI-powered topic generation.
🔎 In this article:
Overview Panel
When you open your project, you’ll be directed to the Overview panel, where you can see the question designated for analysis (e.g., “Why did you give this rating?”).
Here, you'll also find an AI-generated summary, offering a quick view of key themes and patterns in your data.
Click "Start Analysis now" to begin the topic creation process.
Step 1: Choose How to Start
Option 1: Start from Scratch
Select this if you're analyzing this type of open text for the first time. Click "Generate Topics", and the AI will automatically create your initial categories and topics.
Option 2: Import Existing Topics
Use this if you already have a topic collection—either from:
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A previous Caplena project ("Other Project")
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An external Excel file ("Upload File")
Other Project
Choose a previous project you've worked on, or browse team projects (depending on access level). Select the relevant text column, then click "Inherit Topic Collection" to reuse that structure.
This option is ideal if:
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You’re analyzing recurring surveys (e.g., quarterly NPS, monthly product feedback)
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You want consistency across project waves
Upload File
Import your own Excel-based topic collection. Make sure your file matches one of the following formats:
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With sentiment (includes sentiment labels)
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Without sentiment (topic only)
Use this option if you’ve coded data externally (e.g., in Excel or another platform) and want to areuse your work.
Step 2: Decide Whether to Enable Sentiment
Sentiment in Caplena refers to the emotional tone of a response, whether it is positive, neutral, or negative. Enabling sentiment allows the AI to detect not just what people are talking about, but how they feel about it. This provides an extra layer of insight, especially useful in customer or employee feedback analysis.
When sentiment is enabled, each topic can be assigned a sentiment-specific label (e.g., Helpful Staff vs. Unhelpful Staff). This lets you see not only which themes are present, but also how they're perceived.
Enable Sentiment if:
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Your data contains emotional or opinion-based responses (e.g., satisfaction, complaints)
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You're running NPS, CSAT, or employee feedback surveys
Examples:
“The support team was incredibly helpful.” → Customer Service – Positive
“Waiting times were terrible.” → Customer Service – Negative
Do NOT Enable Sentiment if:
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Your data is factual (e.g., improvement suggestions or feature requests)
Example:
“Add a dark mode option.” → No sentiment
“Please include PDF export in the next update.” → No sentiment
Step 3: AI Topic Generation
Once your preferences are set, Caplena generates a structure organized into two levels:
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Categories: Broad themes (e.g., "Quality")
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Topics: Specific concepts (e.g., "Sound Quality ", "Picture Quality")
Pro Tip: The initial AI-generated structure is a starting point, not the final word. Use it to accelerate your analysis, then shape it to your needs. Think of it as a smart draft.
Step 4: Review & Refine
Use this step to validate and improve the AI-generated structure before starting the manual coding process. This is where the Topic Assistant becomes especially helpful, it acts as your intelligent sidekick by identifying:
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Overlapping or similar topics that may need merging
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New trends or emerging ideas in newly added data
By using the Topic Assistant during the refinement phase, you can ensure your topic structure is both comprehensive and efficient.
You'll find these suggestions organized into tabs — such as New Topics, Similar Topics, and Discarded Suggestions — so you can act quickly and keep your structure clean as your dataset grows.
You can also work on:
Categories
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Rename: Adjust wording for clarity
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Remove: Delete irrelevant categories
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Add: Create new ones as needed
Topics
Apply the MECE Principle:
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Mutually Exclusive: No overlap
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Collectively Exhaustive: Cover all relevant points
Use the AI suggestions panel to:
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Merge duplicates
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Refine unclear topics
Example:
If the AI suggests both "Customer Support" and "Help Desk," and they mean the same thing in your context, consider merging them. But if they refer to different touchpoints (e.g., phone vs. chat), keep and clarify.