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0A105G – Introduction to IBM SPSS Text Analytics for IBM SPSS Modeler

Home / 0A105G – Introduction to IBM SPSS Text Analytics for IBM SPSS Modeler

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.

Course Length: 2 day(s)

Course Price: $1400 CAD

Available Course Formats:

  • In-class
  • Instructor Led Online
  • Self-Paced Virtual Classroom

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Course: 0A105G – Introduction to IBM SPSS Text Analytics for IBM SPSS Modeler

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

Audience

This course is for:

  • Anyone who needs to analyze text data for the purpose of creating predictive models or reports
  • Users of IBM SPSS Modeler Text Analytics

Prerequisites

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

Instructor Led In Classroom

Newcomp can directly deliver  IBM Business Analytics courses for Business Intelligence, Performance Management, and IBM Advanced Analytics through the use of in-class training facilities.

Currently,  in-class courses are offered in Markham, Ottawa, Vancouver, Halifax, and Edmonton. Please note that classes can be added to new areas based on demand.

Instructor Led Online

Students receive the same quality as an in-class course, with a live instructor and the ability to participate in hands-on labs through real-life examples

ILOs help cut costs by reducing time and travel as they can be taken from home or the office and require only the use of a computer, high-speed wired internet and a headset.

Self Paced

Students can receive the same high-quality training, with the same courseware at their own speed and schedule with SPVC.  Individuals with busy schedules can complete a course over a 30-day timeframe at a lower price than in-class or ILO courses. Please note that there is no live interaction with an instructor in this format.

On-Demand

This course is available on-demand: request a specific course, location, time frame and/or format by completing this form. This offering increases schedule flexibility and allows us to accommodate people with different availabilities.

Customized Courses

On-site courses can also be customized based on user roles and needs within your organization. This option allows lower costs in travel and time and increased flexibility in scheduling. This format also enables students to work with the organization's data - adding value to the learning experience. This makes the training relevant and applicable to the students; daily tasks and therefore, results in a quicker path to productivity.