0A105: Introduction to IBM SPSS Text Analytics for IBM SPSS Modeler (V16)
DURATION: 2 Days
SCHEDULED DATES: TBD – Contact Us for more Information
DESCRIPTION
Introduction to IBM SPSS Text Analytics for IBM SPSS Modeler (V16) teaches users how to analyze text data using IBM SPSS Modeler Text Analytics. Students will see the complete set of steps involved in working with text data, from reading the text data to creating the final categories for additional analysis. After the final model has been created, there is an example of how to apply the model to perform Churn analysis. Topics include how to automatically and manually create and modify categories, how to edit synonym, type, and exclude dictionaries, and how to perform Text Link Analysis and Cluster Analysis with text data. Also included are examples of how to create resource templates and Text Analysis packages to share work with other projects and other users.
AUDIENCE
This course is for:
– Anyone who needs to analyze text data for the purpose of creating predictive models or reports based in part on text data.
– Users of IBM SPSS Modeler Text Analytics.
PREREQUESITES
You should have:
– General computer literacy
– Practical experience with coding text data is not a prerequisite but would be helpful
You should have completed:
– Introduction to IBM SPSS Modeler and Data Mining course
or experience with IBM SPSS Modeler
OBJECTIVES
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
