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Hello, I’m Terry Woodfield. I’ve been using SAS software since 1978. Since receiving a Ph.D. in statistics in 1982 fromTexas A&M University, I’ve held various academic and consulting positions related to applied analytics, data mining, anddata science. I teach data mining, text mining, predictive modeling, time series forecasting, and other applied analyticscourses offered by SAS Education.

This course, Text Analytics Using SAS Text Miner, will introduce you to an exciting area of applied analytics. Althoughapplications of text analytics have seen outstanding growth over the past 10 years, it has been an established componentof applied analytics for decades. In 1964, Mosteller and Wallace showed how counting words and applying Bayesiantechniques conclusively establishes that James Madison wrote the 12 contested essays of the Federalist Papers. Theypresented one of the first applications of text mining to a specific authorship problem.

I started working in the area of text data mining about 20 years ago. I was working for a small consulting company on aproject to score workers compensation insurance claims for the likelihood of subrogation. Because of serious data qualityissues, my team could not derive models with satisfactory lift until we incorporated text mining strategies that exploitedadjuster notes associated with a claim.

If you’re like most students, you won’t be engaged in authorship research, and you’ll probably not be using text mining asa simple support tool for a larger predictive modeling project. Instead, you might find that text mining provides thecomplete solution for a challenging problem. For example, you might be trying to automate the processing of call centerinteractions so that you can classify calls for routing to the specialists who can best help customers. In the course, you’lllearn specific strategies for tackling problems that fall into two general areas: information retrieval and text categorization.You’ll gain insight into how text mining works by looking at some of the details of latent semantic analysis. Demonstrationsand exercises range from identifying Ted Kaczynski as the author of the Unabomber Manifesto to automatically classifyingaviation safety reports that address runway and taxiway incursions. You’ll search a movie database for movies starringyour favorite actors, and you’ll examine medical abstracts to retrieve specific diagnostic information.

Ideally, to get the most from the class, you should be familiar with the basic functionality of SAS Enterprise Miner.Otherwise, the class has minimal prerequisites. Most topics are self-contained and supply necessary prerequisiteinformation. Because the class is recorded, you can stop the recording if you want to learn more about a topic by using aquick Google search (which, by the way, incorporates elements of latent semantic indexing). Don’t be surprised if aGoogle search sends you right back to the class!

If you have a problem that can be solved using analytics, and if your data include text, this course is meant for you. So,let’s get started!

Course Curriculum

Text Analytics, Time Series, Experimentation, and Optimization Overview.mp4
Text Analytics, Time Series, Experimentation, and Optimization Overview 00:00:00
1.0 Introduction to SAS Enterprise Miner and SAS Text Miner.mp4
1.0 Introduction to SAS Enterprise Miner and SAS Text Miner 00:00:00
1.0 Introduction to SAS Enterprise Miner and SAS Text Miner Objectives
1.0 Introduction to SAS Enterprise Miner and SAS Text Miner Objectives 00:00:00
1.1a What Is Text Analytics?
1.1a What Is Text Analytics? 00:00:00
1.1b What Is Data Mining?
1.1b What Is Data Mining? 00:00:00
1.1c think about it.
1.1c think about it. 00:00:00
1.1d Signal versus Noise
1.1d Signal versus Noise 00:00:00
1.1e Document Separation
1.1e Document Separation 00:00:00
1.2a Analysis Element Organization
1.2a Analysis Element Organization 00:00:00
1.2b Defining a Data Source
1.2b Defining a Data Source 00:00:00
1.2c Variable Roles and Measurement Levels
1.2c Variable Roles and Measurement Levels 00:00:00
1.2d Table Roles
1.2d Table Roles 00:00:00
1.2e Working with Text Mining Data Sources
1.2e Working with Text Mining Data Sources 00:00:00
1.2f Additional Data Sources
1.2f Additional Data Sources 00:00:00
1.3a SAS Enterprise Miner- Interface Tour
1.3a SAS Enterprise Miner- Interface Tour 00:00:00
1.3b Text Parsing Node
1.3b Text Parsing Node 00:00:00
1.3c Text Parsing Node- Stemming Example
1.3c Text Parsing Node- Stemming Example 00:00:00
1.3d Text Parsing Node- Special Tables
1.3d Text Parsing Node- Special Tables 00:00:00
1.3e Text Filter Node
1.3e Text Filter Node 00:00:00
1.3f Text Cluster Node
1.3f Text Cluster Node 00:00:00
1.3g Text Topic Node
1.3g Text Topic Node 00:00:00
1.3h Demo- Text Analytics Illustrated with a Simple Data Set, Part 1
1.3h Demo- Text Analytics Illustrated with a Simple Data Set, Part 1 00:00:00
1.3i Demo- Text Analytics Illustrated with a Simple Data Set, Part 2
00:00
1.3j Demo- Text Analytics Illustrated with a Simple Data Set, Part 3
1.3j Demo- Text Analytics Illustrated with a Simple Data Set, Part 3 00:00:00
1.3k Demo- Text Analytics Illustrated with a Simple Data Set, Part 4
1.3k Demo- Text Analytics Illustrated with a Simple Data Set, Part 4 00:00:00
1.3l Demo- Text Analytics Illustrated with a Simple Data Set, Part 5
1.3l Demo- Text Analytics Illustrated with a Simple Data Set, Part 5 00:00:00
1.3m Exercise- Changing Properties from the Demonstration to See How This Affects Results
1.3m Exercise- Changing Properties from the Demonstration to See How This Affects Results 00:00:00
1.3m Solution- Changing Properties from the Demonstration to See How This Affects Results
1.3m Solution- Changing Properties from the Demonstration to See How This Affects Results 00:00:00
2.0 Overview of Text Analytics Introduction
2.0 Overview of Text Analytics Introduction 00:00:00
2.0 Overview of Text Analytics Objectives
2.0 Overview of Text Analytics Objectives 00:00:00
2.1a SAS Data Access Features
2.1a SAS Data Access Features 00:00:00
2.1b SAS Language Functionality for Processing Text Files
2.1b SAS Language Functionality for Processing Text Files 00:00:00
2.1c Example- A Perl Regular Expression Macro in SAS
2.1c Example- A Perl Regular Expression Macro in SAS 00:00:00
2.1d Running SAS Programs
2.1d Running SAS Programs 00:00:00
2.1e The Text Import Node
2.1e The Text Import Node 00:00:00
2.1f Modifying Imported Data
2.1f Modifying Imported Data 00:00:00
2.1g Demo- Using the Text Import Node
2.1g Demo- Using the Text Import Node 00:00:00
2.1h Excercise-Interpreting the Document Clusters in relation to other variables
2.1h Excercise-Interpreting the Document Clusters in relation to other variables 00:00:00
2.1h Solution-Interpreting the Document Clusters in relation to other variables
2.1h Solution-Interpreting the Document Clusters in relation to other variables 00:00:00
2.2a Introduction
2.2a Introduction 00:00:00
2.2b Stylometry and Forensic Linguistics
2.2b Stylometry and Forensic Linguistics 00:00:00
2.2c Demo- Stylometry for Forensic Linguistics
2.2c Demo- Stylometry for Forensic Linguistics 00:00:00
2.3a Retrieving Information by Filtering and Querying
2.3a Retrieving Information by Filtering and Querying 00:00:00
2.3b Demo- Retrieving Medical Information
2.3b Demo- Retrieving Medical Information 00:00:00
2.3c Excercise-Finding a Text String in a Movies Data Set
2.3c Excercise-Finding a Text String in a Movies Data Set 00:00:00
2.3c Solution-Finding a Text String in a Movies Data Set
2.3c Solution-Finding a Text String in a Movies Data Set 00:00:00
2.3d Exercise-Text Mining SAS Course Description
2.3d Exercise-Text Mining SAS Course Description 00:00:00
2.3d Solution-Text Mining SAS Course Description
2.3d Solution-Text Mining SAS Course Description 00:00:00
3.0 Algorithmic and Methodological Considerations in Text Mining Introduction
3.0 Algorithmic and Methodological Considerations in Text Mining Introduction 00:00:00
3.0 Algorithmic and Methodological Considerations in Text Mining Objectives
3.0 Algorithmic and Methodological Considerations in Text Mining Objectives 00:00:00
3.1a Extracting Informative Text
3.1a Extracting Informative Text 00:00:00
3.1b Concept Linking
3.1b Concept Linking 00:00:00
3.1c Quantifying Text
3.1c Quantifying Text 00:00:00
3.1d Applying Weights to Frequency Counts
3.1d Applying Weights to Frequency Counts 00:00:00
3.1e Term Weight Formulas
3.1e Term Weight Formulas 00:00:00
3.1f Weighted Term-Document Frequency Matrix
3.1f Weighted Term-Document Frequency Matrix 00:00:00
3.2a Applying Singular Value Decomposition
3.2a Applying Singular Value Decomposition 00:00:00
3.2b Singular Value Decomposition Example
3.2b Singular Value Decomposition Example 00:00:00
3.2c Dimensionality Reduction
3.2c Dimensionality Reduction 00:00:00
3.2d Demo- Experimenting with the SVD Dimensions, Part 1
3.2d Demo- Experimenting with the SVD Dimensions, Part 1 00:00:00
3.2e Demo- Experimenting with the SVD Dimensions, Part 2
3.2e Demo- Experimenting with the SVD Dimensions, Part 2 00:00:00
3.2f Exercise- Comparing the Effect of the Number of SVD Dimensions Using Regression Models
3.2f Exercise- Comparing the Effect of the Number of SVD Dimensions Using Regression Models 00:00:00
3.2f Solution- Comparing the Effect of the Number of SVD Dimensions Using Regression Models
3.2f Solution- Comparing the Effect of the Number of SVD Dimensions Using Regression Models 00:00:00
4.0 Additional Ideas and Nodes Introduction
4.0 Additional Ideas and Nodes Introduction 00:00:00
4.0 Additional Ideas and Nodes Objectives
4.0 Additional Ideas and Nodes Objectives 00:00:00
4.1a SAS Enterprise Miner Model Tab
4.1a SAS Enterprise Miner Model Tab 00:00:00
4.1b Training Data and Predictive Models
4.1b Training Data and Predictive Models 00:00:00
4.1c SAS Enterprise Miner Source Data
4.1c SAS Enterprise Miner Source Data 00:00:00
4.1d Predictions Data and Prediction Types for Binary Response Models
4.1d Predictions Data and Prediction Types for Binary Response Models 00:00:00
4.1e SAS Enterprise Miner Input Selection
4.1e SAS Enterprise Miner Input Selection 00:00:00
4.1f Model Assessment
4.1f Model Assessment 00:00:00
4.1g A Trade-Off- Models with Greater Predictive Power versus Greater Interpretability
4.1g A Trade-Off- Models with Greater Predictive Power versus Greater Interpretability 00:00:00
4.1h Demo- Experimenting with the Effects of Global Weights on Predictive Power
4.1h Demo- Experimenting with the Effects of Global Weights on Predictive Power 00:00:00
4.1i Exercise- Adding a Decision Tree Node to the Flow to Compare to the Previous Four Regression Models
4.1i Exercise- Adding a Decision Tree Node to the Flow to Compare to the Previous Four Regression Models 00:00:00
4.1i Solution- Adding a Decision Tree Node to the Flow to Compare to the Previous Four Regression Models
4.1i Solution- Adding a Decision Tree Node to the Flow to Compare to the Previous Four Regression Models 00:00:00
4.2a Text Rule Builder Node- Introduction
4.2a Text Rule Builder Node- Introduction 00:00:00
4.2b Text Rule Builder Node Train Properties
4.2b Text Rule Builder Node Train Properties 00:00:00
4.2c Text Rule Builder Node Score Properties
4.2c Text Rule Builder Node Score Properties 00:00:00
4.2d Demo- Predictive Modeling Using the Text Rule Builder Node, Part 1
4.2d Demo- Predictive Modeling Using the Text Rule Builder Node, Part 1 00:00:00
4.2e Demo- Predictive Modeling Using the Text Rule Builder Node, Part 2
4.2e Demo- Predictive Modeling Using the Text Rule Builder Node, Part 2 00:00:00
4.2f Demo- Predictive Modeling Using the Text Rule Builder Node, Part 3
4.2f Demo- Predictive Modeling Using the Text Rule Builder Node, Part 3 00:00:00
4.3a Scenario- Text Mining 12 Million Documents
4.3a Scenario- Text Mining 12 Million Documents 00:00:00
4.3b High-Performance Text Mining
4.3b High-Performance Text Mining 00:00:00
4.3c HP Modes of Operation
4.3c HP Modes of Operation 00:00:00
4.3d High-Performance Nodes
4.3d High-Performance Nodes 00:00:00
4.3e HP Text Miner Node Properties
4.3e HP Text Miner Node Properties 00:00:00
4.3f Data Set Requirements and Process Flow
4.3f Data Set Requirements and Process Flow 00:00:00
4.3g Results for the HP Text Miner Node
4.3g Results for the HP Text Miner Node 00:00:00
4.3h Demo- Predictive Modeling with the HP Text Miner Node
4.3h Demo- Predictive Modeling with the HP Text Miner Node 00:00:00
4.3i PROC HPTMINE Syntax
4.3i PROC HPTMINE Syntax 00:00:00
4.3j Demo- Using PROC HPTMINE
4.3j Demo- Using PROC HPTMINE 00:00:00
4.3k HP Data Partition Node
4.3k HP Data Partition Node 00:00:00
4.3l Demo- Predictive Modeling Using High-Performance Nodes
4.3l Demo- Predictive Modeling Using High-Performance Nodes 00:00:00
4.3m Exercise- Using the HP Data Partition, HP Text Miner, and HP Tree Nodes
4.3m Exercise- Using the HP Data Partition, HP Text Miner, and HP Tree Nodes 00:00:00
4.3m Solution- Using the HP Data Partition, HP Text Miner, and HP Tree Nodes
4.3m Solution- Using the HP Data Partition, HP Text Miner, and HP Tree Nodes 00:00:00
4.3n Demo- Relationship between Information Retrieval and Text Categorization, Part 1
4.3n Demo- Relationship between Information Retrieval and Text Categorization, Part 1 00:00:00
4.3o Demo- Relationship between Information Retrieval and Text Categorization, Part 2
4.3o Demo- Relationship between Information Retrieval and Text Categorization, Part 2 00:00:00
Course Notes: Text Analytics Using SAS Text Miner
Course Notes: Text Analytics Using SAS Text Miner 00:00:00

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