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Documentation Index

Fetch the complete documentation index at: https://docs.caplena.com/llms.txt

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Semi-open mode is designed for structured open-text questions where responses follow a limited and repeatable pattern — think brand awareness surveys, competitor lists, or short 1–3 word answers. Rather than exploring themes, Semi-Open mode focuses on standardizing similar answers, grouping variations of the same term, and creating clean, consistent categories.
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Typical use cases:
  • Brand awareness (“Which brands do you know?”)
  • List-based answers (products, competitors, sources)
  • Short, repetitive responses

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Step 1: Select Your Text Columns

Before starting the analysis, decide how to handle your columns:

Analyze Separately

Select each column as Text-to-Analyze (TTA). Use this when you want to compare results across questions.
Each TTA column is analyzed separately and consumes credits.

Combine Columns

Use Smart Columns to merge multiple columns into one unified analysis. Best when columns contain the same or very similar question.Smart Columns guide →

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Step 2: Start the Analysis

Starting analysis
Choose how to build your topic structure:
  • Start from scratch – AI generates initial topics and categories automatically
  • Reuse existing structure – Import a topic collection or reuse categories from another project

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Step 3: Configure Key Settings

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Term Similarity Controls how strictly terms are grouped together.
ValueBehavior
100%Exact matches only
~75%Groups similar variations (e.g. spelling differences)
Minimum Count Defines how often a term must appear to become its own topic. For example, setting this to 2 means only terms mentioned at least twice will appear as separate topics. “Other” Topic Controls how low-frequency responses are handled:
OptionDescription
NoneNo “Other” topic created
Unassigned RowsOnly responses without any topic
Rare TermsLow-frequency terms grouped into “Other”

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Step 4: Review Topics & Keywords

Before finalizing, review and fine-tune your topics and keywords to ensure accurate results. Review Topics
  • Confirm each topic represents a distinct answer or concept
  • Rename topics to improve clarity
  • Merge duplicates and remove irrelevant topics
Adjust Keywords Each topic uses keywords to assign responses correctly. To edit them, click on a topic and open the keyword list.
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Best practices for keywords:
  • Include abbreviations – e.g. “FB” for Facebook
  • Add spelling variations – e.g. “McDonalds”, “Mc Donald’s”
  • Include common phrasing – different ways users might refer to the same concept
  • Be specific – especially for similar or overlapping topics
Punctuation doesn’t matter — “Iqos, TEREA, Plum” works the same as “Iqos TEREA Plum”. Only || is treated as a separator.
How keyword matching works The system always matches the most specific keyword in the text. If two keywords overlap, the longer one wins.
Example: Keywords are Tesla and Tesla Model 3. For the text “I like my Tesla Model 3”, the system matches Tesla Model 3 ✅ and ignores Tesla since the longer keyword already covers it.
In Semi-Open mode, you do not need to review every individual response. Focus on making sure topics are correct and keywords capture all relevant variations — this keeps your analysis efficient and scalable.

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Video Walkthrough

Last modified on May 22, 2026