Introduction to IBM SPSS Modeler Text Analytics (v18.1.1) SPVC - 0E108G

Beschreibung

This course (formerly: Introduction to IBM SPSS Text Analytics for IBM SPSS Modeler (v18)) teaches you how to analyze text data using IBM SPSS Modeler Text Analytics. You will be introduced to 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 in telecommunications. 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 tempates and Text Analysis packages to share with other projects and other users.

If you are enrolling in a Self Paced Virtual Classroom or Web Based Training course, before you enroll, please review the Self-Paced Virtual Classes and Web-Based Training Classes on our Terms and Conditions page, as well as the system requirements, to ensure that your system meets the minimum requirements for this course. http://www.ibm.com/training/terms

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

Ziel

Please refer to course overview

Anforderungen an die Teilnehmer

General computer literacy
Prior completion of Introduction to IBM SPSS Modeler and Data Science (v18.1.1) is recommended.

Teilnehmer

Users of IBM SPSS Modeler responsible for building predictive models who want to leverage the full potential of classification models in IBM SPSS Modeler.