Using the Text Mining node in a stream

The Text Mining modeling node is used to access data and extract concepts in a stream. You can use any source node to access data, such as a Database node, Var. File node, Web Feed node, or Fixed File node. For text that resides in external documents, a File List node can be used.

Example 1: File List node and Text Mining node to build a concept model nugget directly

The following example shows how to use the File list node along with the Text Mining modeling node to generate the concept model nugget. For more information on using the File List node, see File List node.

  1. File List node (Settings tab). First, we added this node to the stream to specify where the text documents are stored. We selected the directory containing all of the documents on which we want to perform text mining.
  2. Text Mining node (Fields tab). Next, we added and connected a Text Mining node to the File List node. In this node, we defined our input format, resource template, and output format. We selected the field name produced from the File List node and selected the text field, as well as other settings. See the topic Using the Text Mining node in a stream for more information.
  3. Text Mining node (Model tab). Next, on the Model tab, we selected the build mode to generate a concept model nugget directly from this node. You can select a different resource template, or keep the basic resources.

Example 2: Excel File and Text Mining nodes to build a category model interactively

This example shows how the Text Mining node can also launch an interactive workbench session. For more information on the interactive workbench, see Interactive workbench mode.

  1. Excel source node (Data tab). First, we added this node to the stream to specify where the text is stored.
  2. Text Mining node (Fields tab). Next, we added and connected a Text Mining node. On this first tab, we defined our input format. We selected a field name from the source node.
  3. Text Mining node (Model tab). Next, on the Model tab, we selected to build a category model nugget interactively and to use the extraction results to build categories automatically. In this example, we loaded a copy of resources and a set of categories from a text analysis package.
  4. Interactive Workbench session. Next, we executed the stream, and the interactive workbench interface opened. After an extraction was performed, we began exploring our data and improving our categories.