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Introduction to IBM SPSS Text Analytics for IBM SPSS Modeler (V16) (0A105G)

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Detaillierter Kursinhalt

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
An Overview of Text Mining in IBM SPSS Modeler
  • Explain the text mining nodes available in Modeler
  • Complete a typical text mining modeling session
Reading Text Data
  • Read text from documents
  • View text from documents within Modeler
  • Read text from Web Feeds
Linguistic Analysis and Text Mining
  • Describe linguistic analysis
  • Describe the process of text extraction
  • Describe categorization of terms and concepts
  • Describe Templates and Libraries
  • Describe Text Analysis Packages
Creating a Text Mining Concept Model
  • Develop a text mining concept model
  • Compare models based on using different Resource Templates
  • Score model data
  • Analyze model results
Reviewing Types and Concepts in the Interactive Workbench
  • Use the Interactive Workbench
  • Review extracted concepts
  • Review extracted types
  • Update the modeling node
Editing Linguistic Resources
  • Linguistic Editing Preparation
  • Develop editing strategy
  • Add Type definitions
  • Add Synonym definitions
  • Add Exclusion definitions
  • Text re-extraction to review modifications
Fine Tuning Resources
  • Review Advanced Resources
  • Adding fuzzy grouping exceptions
  • Adding non-Linguistic entities
  • Extracting non-Linguistic entities
  • Forcing a word to take a particular part of speech
Performing Text Link Analysis
  • Use Text Link Analysis interactively
  • Use visualization pane
  • Use Text Link Analysis node
  • Create categories from a pattern
  • Create text link rules
Clustering Concepts
  • Create clusters
  • Use visualization pane
  • Create categories from a cluster
Categorization Techniques
  • Describe approaches to categorization
  • Describe linguistic based categorization
  • Describe frequency based categorization
  • Describe results of different categorization methods
Creating Categories
  • Develop categorization strategy
  • Create categories automatically
  • Create categories manually
  • Use conditional rules to create categories
  • Assess category overlap
  • Extend categories
  • Import coding frames
  • Create Text Analysis Packages
Managing Linguistic Resources
  • Use the Template Editor
  • Save resource templates
  • Describe local and public libraries
  • Add libraries
  • Publishing libraries
  • Share libraries
  • Share templates
  • Backup resources
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
  • Overview of Text Mining process