Introduction to IBM SPSS Modeler Text Analytics (V15) Eğitimi

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Introduction to IBM SPSS Modeler Text Analytics (V15) (0A104G)

IBM Course Code: 0E104G

Introduction to IBM SPSS Modeler Text Analytics is a two-day instructor led classroom course that 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.

Who Needs to Attend

  • 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

Prerequisites

You should have completed:

  • "Introduction to IBM SPSS �Modeler and Data Mining" course or have experience with IBM SPSS Modeler

You should have:

  • General computer literacy

Practical experience with coding text data is not a prerequisite but would be helpful.

Follow-On Courses

There are no follow-ons for this course.

Course Outline

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

A Text Mining Example

  • 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

Extracted Results in the Interactive Workbench

  • Use the Interactive Workbench
  • Review extracted concepts
  • Review extracted types
  • Update the modeling node

Linguistic Resources

  • Describe the resource template
  • Review libraries
  • Review Dictionaries
  • Manage libraries

Editing Dictionaries

  • Linguistic Editing Preparation
  • Develop editing strategy
  • Add Type definitions
  • Add Synonym definitions
  • Add Exclusion definitions
  • Text re-extraction to review modifications

Editing Advanced Resources

  • Review Advanced Resources
  • Add fuzzy grouping exceptions
  • Review Text Link Rules

Text Link Analysis

  • Use Text Link Analysis interactively
  • Use visualization pane
  • Use Text Link Analysis node
  • Create categories from a pattern

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

Managing Linguistic Resources

  • Use the Template Editor
  • Save resource templates
  • Describe local and public libraries
  • Publishing libraries
  • Share libraries
  • Share templates
  • Create Text Analysis Packages
  • Backup resources

Using Text Mining Models

  • Explore text mining models
  • Develop a model with quantitative and qualitative data
  • Score new data



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