Category data

On the Categories tab, you can build and manage your categories. After extracting the concepts and types from your text data, you can begin building categories automatically using techniques such as concept inclusion, semantic network (in English only), or manually.

Since this example flow is built using a text analysis package (TAP), the category model is already populated.

Each time a category is created or updated, you can see whether any text matches a descriptor in a given category by clicking Score to score the documents or records. If a match is found, the document or record is assigned to that category. The end result is that most, if not all, of the documents or records are assigned to categories based on the descriptors in the categories.

You can expand each category or sub-category, and select them or a descriptor to see the source data:

Concepts data

During the extraction process, the text data is analyzed to identify interesting or relevant single words such as airport, location, or locatoin, and word phrases such as airport pick-up. These words and phrases are collectively referred to as terms. Using the linguistic resources, the relevant terms are extracted, and similar terms are grouped together under a lead term called a concept.

In this way, a concept might represent multiple underlying terms depending on your text and the set of linguistic resources you're using.

You can also use a Filter to select a subset of concepts:

And you can select from different options:

If you want to remove the Filter and display all concepts, select Clear Filter.

Text link analysis

1. Access the text links from the Text links tab:
2. Select a Type Pattern (for example, <Services> + <Positive>) to display the corresponding document preview. As the text in the Document preview cell may be truncated, you can click on a cell to display the entire text.