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