> ## Documentation Index
> Fetch the complete documentation index at: https://docs.caplena.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Semi-Open Mode: Quick Guide
> Analyze list-based responses quickly and consistently.
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.
**Typical use cases:**
* Brand awareness ("Which brands do you know?")
* List-based answers (products, competitors, sources)
* Short, repetitive responses
***
## Step 1: Select Your Text Columns
Before starting the analysis, decide how to handle your columns:
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.
Use **Smart Columns** to merge multiple columns into one unified analysis. Best when columns contain the **same or very similar question**.
[Smart Columns guide →](/data-project-management/common-smart-columns-intro/how-to-combine-multiple-columns-for-joint-analysis)
***
## Step 2: Start the 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
***
## Step 3: Configure Key Settings
**Term Similarity**
Controls how strictly terms are grouped together.
| Value | Behavior |
| ----- | ----------------------------------------------------- |
| 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:
| Option | Description |
| --------------- | ---------------------------------------- |
| None | No "Other" topic created |
| Unassigned Rows | Only responses without any topic |
| Rare Terms | Low-frequency terms grouped into "Other" |
***
## 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.
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
Keywords have a maximum length of 30 characters including spaces. If a keyword exceeds this limit, it will not trigger assignments — shorten it or split it into a shorter variation.
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.
## Video Walkthrough
***
**Typical use cases:**
* Brand awareness ("Which brands do you know?")
* List-based answers (products, competitors, sources)
* Short, repetitive responses
***
## Step 1: Select Your Text Columns
Before starting the analysis, decide how to handle your columns:
.gif?s=1ecc784c873394534233421c78966fd9)
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
***
## Step 3: Configure Key Settings
**Term Similarity**
Controls how strictly terms are grouped together.
| Value | Behavior |
| ----- | ----------------------------------------------------- |
| 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:
| Option | Description |
| --------------- | ---------------------------------------- |
| None | No "Other" topic created |
| Unassigned Rows | Only responses without any topic |
| Rare Terms | Low-frequency terms grouped into "Other" |
***
## 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.
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