Starting the analysis

Once your data is in Caplena, uploaded manually or via integration (Google, Amazon, Qualtrics, API, etc.), you’re ready to start your analysis.

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Overview panel

When you open your project, you’ll land on the Overview panel, where you can see the question designated for analysis and an AI-generated summary of key themes. Click “Start analysis now” to begin.

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Step 1: Choose how to start

Start from scratch The AI automatically generates a topic structure based on your data. Best if you’re analyzing this type of feedback for the first time. Import existing topics Reuse a topic collection you already have, either from a previous Caplena project or an Excel file.

• Other project - Choose a previous project, select the relevant text column, and click “Inherit topic collection”. Ideal for recurring surveys (e.g. quarterly NPS, monthly product feedback) where consistency across waves matters. For large-scale recurring projects, see Tracker Setup: Best Practices →.
• Upload File - Import your own topic collection from a CSV or Excel file. Supported columns:
  • topic (required) - The topic name.
  • category (optional) - The category the topic belongs to.
  • code (optional) - A unique numerical ID for the topic.
  • sentiment (optional) - The sentiment setting for the topic.
  • description (optional) - A longer explanation of when the topic applies. Used by the AI as a primary input when assigning topics, so clear descriptions noticeably improve assignment quality.
  • keywords (optional) - Comma-separated keywords for the topic. Used in Semi-Open mode to match responses. Each keyword must be 30 characters or fewer (including spaces) — imports with longer keywords are rejected with an error, so shorten or split them before uploading. Your file must match one of these formats. With sentiment:
  
  Without sentiment:
  
  Include topic descriptions when uploading. Descriptions tell the AI when a topic should apply, not just what it’s called. A topic like Pricing becomes much more reliable when paired with a description such as “Mentions of cost, price, fees, or value for money — including comments about discounts and subscription tiers.” If you leave the column empty, Caplena auto-generates descriptions on the first run, but uploading your own keeps assignments aligned with how your team defines each topic.
  Example with descriptions:

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Step 2: Enable sentiment (optional)

Sentiment detection classifies the emotional tone of each response as positive, neutral, or negative. When enabled, a topic like Customer service splits into Customer service – positive and Customer service – negative, so you can see not just what people mention but how they feel about it. Turn it on if your data contains opinions or emotional responses — NPS comments, CSAT surveys, employee feedback:

“The support team was incredibly helpful.” → Customer Service – Positive “Waiting times were terrible.” → Customer Service – Negative

Skip it if your data is factual — feature requests, improvement suggestions, etc.:

“Add a dark mode option.” → No sentiment needed “Please include PDF export in the next update.” → No sentiment needed

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Step 3: Generate topics

Caplena generates a MECE topic collection, mutually exclusive and collectively exhaustive, so topics cover all relevant feedback without overlap. Initial topic collection The AI generates a draft collection of 20–60 topics grouped into categories, based on up to 20,000 rows of your data plus your project name, description, and question context. Each topic includes an auto-generated description you can edit. You’ll also see:

• Rare topics - not included by default, but available to browse and add manually
• Similar topics - flagged for potential merging or removal

Prompt-based generation You can guide how topics are created using a custom prompt, useful when you want to focus on a specific theme or apply your own framework. Once you set a prompt it becomes your default, used when editing topics and when new rows are uploaded. You can override it anytime for a one-off run. When you’re happy with the topic collection, click Done. The AI will begin assigning topics within 1–2 minutes. Learn more about prompt-based generation →

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Step 4: Review and refine

How much manual tweaking you’ll need depends on which AI model you’re using: New AI — typically delivers human-level quality after the first run; only light adjustments needed. Learn more → Legacy AI — may require more hands-on review: merging overlaps, clarifying vague topics, adding missing ones. Learn more →