Mining for text links

The Text Link Analysis (TLA) node adds pattern-matching technology to text mining's concept extraction in order to identify relationships between the concepts in the text data based on known patterns. These relationships can describe how a customer feels about a product, which companies are doing business together, or even the relationships between genes or pharmaceutical agents.

For example, extracting your competitor’s product name may not be interesting enough to you. Using this node, you could also learn how people feel about this product, if such opinions exist in the data. The relationships and associations are identified and extracted by matching known patterns to your text data.

You can use the TLA pattern rules inside certain resource templates shipped with Text Analytics or create/edit your own. Pattern rules are made up of macros, word lists, and word gaps to form a Boolean query, or rule, that is compared to your input text. Whenever a TLA pattern rule matches text, this text can be extracted as a TLA result and restructured as output data.

The Text Link Analysis node offers a more direct way to identify and extract TLA pattern results from your text and then add the results to the dataset in the flow. But the Text Link Analysis node is not the only way in which you can perform text link analysis. You can also use an interactive workbench session in the Text Mining modeling node.

In the interactive workbench, you can explore the TLA pattern results and use them as category descriptors and/or to learn more about the results using drill-down and graphs. In fact, using the Text Mining node to extract TLA results is a great way to explore and fine-tune templates to your data for later use directly in the TLA node.

The output can be represented in up to 6 slots, or parts.

You can find this node under the Text Analytics section of the node palette.

Requirements. The Text Link Analysis node accepts text data read into a field using an Input node.

Strengths. The Text Link Analysis node goes beyond basic concept extraction to provide information about the relationships between concepts, as well as related opinions or qualifiers that may be revealed in the data.