Topical analysis plays a crucial role in gaining insights from qualitative data. When conducting topical analysis, you have the option of using standard topics, Custom Themes, or a combination of both.

This article aims to clarify the difference between these approaches and help you choose the most suitable one for your needs.

A topic is a classification under which words are grouped, based on a shared or mental concept. For example, the topic 'Building components' contains words such as room, floor, ceiling, and many other similar words.

Topics are designed to provide an effortless and ready-to-use solution for topical analysis. No coding is required on your part. With topics, you can start analyzing your data right away.

On the other hand, Custom Themes offer a more flexible and personalized approach. Custom Themes allow you to create your own categories for text analysis. By selecting specific topics, words, and phrases that you want the model to focus on, you can achieve more accurate insights suited to your industry.

While the outcome of both topics and Custom Themes is similar, the latter allows you to build them according to your specific business needs. This means that you have the ability to categorize words or phrases based exactly on how they are used within your industry.

The key distinction between topics and Custom Themes lies in the scope of elements they incorporate.

Topics primarily revolve around individual words. However, Custom Themes go beyond words and allow you to include a wider range of elements in your analysis. This can include grammatical categories, phrases, and other topics. Such flexibility enables you to dive deeper into the nuances and subtleties of your textual data.

It is important to note that using Custom Themes does not alter the outcome of the topical analysis.

The calculations for topics and Custom Themes are conducted independently, ensuring that you have consistent and reliable results from both approaches. Therefore, you can leverage the benefits of Custom Themes without affecting the accuracy and validity of your topic analysis.