Schulung - IBM 0A108G - Introduction to IBM SPSS Modeler Text Analytics (v18.1.1)

Course Outline

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

Objective

Please refer to course overview