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 customers 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