This course (formerly: Introduction to IBM SPSS Text Analytics for IBM SPSS Modeler (v18)) teaches you how to analyze text data using IBM SPSS Modeler Text Analytics. You will be introduced to the complete set of steps involved in working with text data, from reading the text data to creating the final categories for additional analysis. After the final model has been created, there is an example of how to apply the model to perform churn analysis in telecommunications. Topics include how to automatically and manually create and modify categories, how to edit synonym, type, and exclude dictionaries, and how to perform Text Link Analysis and Cluster Analysis with text data. Also included are examples of how to create resource tempates and Text Analysis packages to share with other projects and other users.
• General computer literacy
• Prior completion of Introduction to IBM SPSS Modeler and Data Science (v18.1.1) is recommended.
Please refer to course overview
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 project
Unit 2 - An overview of text mining
• Describe the nodes that were specifically developed for text mining
• Complete a typical text mining modeling session
Unit 3 - Reading text data
• Reading text from multiple files
• Reading text from Web Feeds
• Viewing text from documents within Modeler
Unit 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 concepts
Unit 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 results
Unit 6 - Reviewing types and concepts in the Interactive Workbench
• Use the Interactive Workbench
• Update the modeling node
• Review extracted concepts
Unit 7 - Editing linguistic resources
• Describe the resource template
• Review dictionaries
• Review libraries
• Manage libraries
Unit 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 entities
Unit 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 node
Unit 10 - Clustering concepts
• Create Clusters
• Creating categories from cluster concepts
• Fine tuning Cluster Analysis settings
Unit 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 Techniques
Unit 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 Categories
Unit 13 - Managing Linguistic Resources
• Use the Template Editor
• Share Libraries
• Save resource templates
• Share Templates
• Describe local and public libraries
• Backup Resources
• Publishing libraries
Unit 14 - Using text mining models
• Explore text mining models
• Develop a model with quantitative and qualitative data
• Score new data
Appendix A - The process of text mining
• Explain the steps that are involved in performing a text mining project
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