Introduction to IBM SPSS Modeler Text Analytics (v18.1.1) SPVC

Unit 1 - Introduction to text mining• Describe text mining and its relationship to data mining• Explain CRISP-DM methodology as it applies to text mining• Describe the steps in a text mining projectUnit 2 - An overview of text mining• Describe the nodes that were specifically developed for text mining• Complete a typical text mining modeling sessionUnit 3 - Reading text data• Reading text from multiple files• Reading text from Web Feeds• Viewing text from documents within ModelerUnit 4 - Linguistic analysis and text mining• Describe linguistic analysis• Describe Templates and Libraries• Describe the process of text extraction• Describe Text Analysis Packages• Describe categorization of terms and conceptsUnit 5 - Creating a text mining concept model• Develop a text mining concept model• Score model data• Compare models based on using different Resource Templates• Merge the  results with a file containing the customer’s demographics• Analyze model resultsUnit 6 - Reviewing types and concepts in the Interactive Workbench• Use the Interactive Workbench• Update the modeling node• Review extracted conceptsUnit 7 - Editing linguistic resources• Describe the resource template• Review dictionaries• Review libraries• Manage librariesUnit 8 - Fine tuning resources• Review Advanced Resources• Extracting non-linguistic entities• Adding fuzzy grouping exceptions• Forcing a word to take a particular Part of Speech• Adding non-Linguistic entitiesUnit 9 - Performing Text Link Analysis• Use Text Link Analysis interactively• Create categories from a pattern• Use the visualization pane• Create text link rules• Use the Text Link Analysis nodeUnit 10 - Clustering concepts• Create Clusters• Creating categories from cluster concepts• Fine tuning Cluster Analysis settingsUnit 11 - Categorization techniques• Describe approaches to categorization• Use Frequency Based Categorization• Use Text Analysis Packages to Categorize data• Import pre-existing categories from a Microsoft Excel file• Use Automated Categorization with Linguistic-based TechniquesUnit 12 - Creating categories• Develop categorization strategy• Fine turning the categories• Importing pre-existing categories• Creating a Text Analysis Package• Assess category overlap• Using a Text Analysis Package to categorize a new set of data• Using Linguistic Categorization techniques to Creating CategoriesUnit 13 - Managing Linguistic Resources• Use the Template Editor• Share Libraries• Save resource templates• Share Templates• Describe local and public libraries• Backup Resources• Publishing librariesUnit 14 - Using text mining models• Explore text mining models• Develop a model with quantitative and qualitative data• Score new dataAppendix A - The process of text mining• Explain the steps that are involved in performing a text mining project

Users of IBM SPSS Modeler responsible for building predictive models who want to leverage the full potential of classification models in IBM SPSS Modeler.

• General computer literacy• Prior completion of Introduction to IBM SPSS Modeler and Data Science (v18.1.1) is recommended.

Please refer to course overview