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

General computer literacy

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

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