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

Course Outline

Unit 1 - Introduction to text miningDescribe text mining and its relationship to data miningExplain CRISP-DM methodology as it applies to text miningDescribe the steps in a text mining projectUnit 2 - An overview of text miningDescribe the nodes that were specifically developed for text miningComplete a typical text mining modeling sessionUnit 3 - Reading text dataReading text from multiple filesReading text from Web FeedsViewing text from documents within ModelerUnit 4 - Linguistic analysis and text miningDescribe linguistic analysisDescribe Templates and LibrariesDescribe the process of text extractionDescribe Text Analysis PackagesDescribe categorization of terms and conceptsUnit 5 - Creating a text mining concept modelDevelop a text mining concept modelScore model dataCompare models based on using different Resource TemplatesMerge the  results with a file containing the customer’s demographicsAnalyze model resultsUnit 6 - Reviewing types and concepts in the Interactive WorkbenchUse the Interactive WorkbenchUpdate the modeling nodeReview extracted conceptsUnit 7 - Editing linguistic resourcesDescribe the resource templateReview dictionariesReview librariesManage librariesUnit 8 - Fine tuning resourcesReview Advanced ResourcesExtracting non-linguistic entitiesAdding fuzzy grouping exceptionsForcing a word to take a particular Part of SpeechAdding non-Linguistic entitiesUnit 9 - Performing Text Link AnalysisUse Text Link Analysis interactivelyCreate categories from a patternUse the visualization paneCreate text link rulesUse the Text Link Analysis nodeUnit 10 - Clustering conceptsCreate ClustersCreating categories from cluster conceptsFine tuning Cluster Analysis settingsUnit 11 - Categorization techniquesDescribe approaches to categorizationUse Frequency Based CategorizationUse Text Analysis Packages to Categorize dataImport pre-existing categories from a Microsoft Excel fileUse Automated Categorization with Linguistic-based TechniquesUnit 12 - Creating categoriesDevelop categorization strategyFine turning the categoriesImporting pre-existing categoriesCreating a Text Analysis PackageAssess category overlapUsing a Text Analysis Package to categorize a new set of dataUsing Linguistic Categorization techniques to Creating CategoriesUnit 13 - Managing Linguistic ResourcesUse the Template EditorShare LibrariesSave resource templatesShare TemplatesDescribe local and public librariesBackup ResourcesPublishing librariesUnit 14 - Using text mining modelsExplore text mining modelsDevelop a model with quantitative and qualitative dataScore new dataAppendix A - The process of text miningExplain the steps that are involved in performing a text mining project

Objective

Please refer to course overview