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Text Mining node (SPSS Modeler)

Text Mining node

Figure 1. Text Mining node to analyze comments from hotel guests
Text Mining node to analyze comments from hotel guests
  1. Add a Data Asset node that points to hotelSatisfaction.csv.
  2. From the Text Analytics category on the node palette, add a Text Mining node, connect it to the Data Asset node you added in the previous step, and double-click it to open its properties.
  3. Under Fields, select Comments for the Text field and select id for the ID field. Note that only the Text field is required.
    Figure 2. Text Mining node properties
    Text Mining node build properties
  4. Under Copy resources from, select Text analysis package, click Select Resources, and then load Hotel Satisfaction (English).tap (with Current category set(s) = Topic + Opinion).
    A text analysis package (TAP) is a predefined set of libraries and advanced linguistic and nonlinguistic resources bundled with one or more sets of predefined categories. If no text analysis package is relevant for your application, you can instead start by selecting Resource template under Copy resources from. A resource template is a predefined set of libraries and advanced linguistic and nonlinguistic resources that have been fine-tuned for a particular domain or usage.
    Figure 3. Text Mining node properties
    Text Mining node build properties
  5. Under Build models, make sure Build interactively (category model nugget) is selected. Later when you run the node, this option will launch an interactive interface (known as the Text Analytics Workbench) in which you can extract concepts and patterns, explore and fine-tune the extracted results, build and refine categories, and build category model nuggets.
  6. Under Begin session by, select Extracting concepts and text links. The option Extracting concepts extracts only concepts, whereas TLA extraction outputs both concepts and text links that are connections between topics (service, personnel, food, etc.) and opinions.
  7. Under Expert, select Accommodate spelling for a minimum word character length of. This option applies a fuzzy grouping technique that helps group commonly misspelled words or closely spelled words under one concept. The fuzzy grouping algorithm temporarily strips all vowels (except the first one) and strips double/triple consonants from extracted words and then compares them to see if they're the same (so, for example, location and locatoin are grouped together).
    Figure 4. Text Mining node properties
    Text Mining node expert properties
  8. Click Save. Right-click the Text Mining node and run it to open the Text Analytics Workbench and proceed to the next section of this tutorial.
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