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Introduction to IBM SPSS Modeler Text Analytics (v18.1.1) (0A108G)

Course Description Schedule Course Outline

Detailed Course Outline

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


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