Digi Academy

Introduction to IBM SPSS Text Analytics for IBM SPSS Modeler (V16)

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A Milano

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Informazioni importanti

Tipologia Corso
Luogo Milano
Inizio Scegli data
  • Corso
  • Milano
  • Inizio:
    Scegli data
Descrizione

Prerequisiti
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
Target del corso
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.

Strutture (1)
Dove e quando
Inizio Luogo
Scegli data
Milano
Via Valtellina, 63, 20124, Milano, Italia
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Inizio Scegli data
Luogo
Milano
Via Valtellina, 63, 20124, Milano, Italia
Visualizza mappa

Cosa impari in questo corso?

Data Mining

Programma

dettagliati del corso
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

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