> ## 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. # Key concepts: Categories, topics & sentiment > Understand the building blocks of analysis in Caplena before you start. Caplena analyzes open-ended feedback by organizing it into categories, topics, and (optionally) sentiment. Getting familiar with these three concepts will make everything else click faster. ### Category A **category** is a high-level theme that groups related topics together, for example `Customer Service`, `Product Quality`, or `Pricing`. Categories Categories are taken into account by the AI when assigning topics. ### Topic A **topic** is a specific idea or theme within a categorym what someone is actually talking about in their response. Topics are also referred to as codes, themes, or labels. Examples within a Customer Service category: * `Support Responsiveness` * `Refund Process` * `Property Communication` #### Topic label The **topic label** is the short name you give a topic. It appears on topic chips throughout Caplena and is what the AI uses to recognize and assign the topic. The label directly affects AI accuracy, keep it clear and specific. #### Topic description A longer explanation of what the topic covers and when it should apply. Descriptions give the AI more context and are especially important for topics that might otherwise overlap. Topic description Topic descriptions are used by the AI when assigning topics, the more precise, the better the results. #### Code A **code** is a unique numerical ID assigned to each topic. Caplena assigns one automatically, but you can set it manually if you need to match an existing coding scheme. Screenshot 2026 05 19 At 20 36 25 ### Sentiment **Sentiment** adds emotional context to topics, classifying each response as **positive**, **neutral**, or **negative**. Instead of just knowing what someone talked about, you also know how they felt about it. Sentiment For example: * `Support / Helpfulness – Positive` → "The team was incredibly helpful" * `Support / Helpfulness – Negative` → "No one responded to my request" You only need to create one topic (e.g. `Support`), the AI assigns the sentiment automatically. **Sentiment is optional.** Turn it on for opinion-based data like NPS or CSAT. Skip it for factual data like feature requests or open suggestions. #### Disabling sentiment If you disable sentiment on a topic, all sentiment labels are permanently removed. This cannot be undone.