Contents
Chapter 1 Introduction to Text Analytics
Text Mining Using SAS Text Miner
Enhancing Predictive Models Using Exploratory Text Mining
Chapter 2 Information Extraction Using SAS Crawler
Introduction to Information Extraction and Organization
SAS Information Retrieval Studio Interface
Web Crawling: Real-World Applications and Examples
Understanding Core Component Servers
Component Servers of SAS Search and Indexing
Chapter 3 Importing Textual Data into SAS Text Miner
An Introduction to SAS Enterprise Miner and SAS Text Miner
Data Types, Roles, and Levels in SAS Text Miner
Creating a Data Source in SAS Enterprise Miner
Importing Textual Data into SAS
Importing Data into SAS Text Miner Using the Text Import Node
Importing XLS and XML Files into SAS Text Miner
Managing Text Using SAS Character Functions
Chapter 4 Parsing and Extracting Features
Text Parsing Node in SAS Text Miner
Building Custom Entities Using SAS Contextual Extraction Studio
Chapter 6 Clustering and Topic Extraction
Singular Value Decomposition and Latent Semantic Indexing
Comparison of Statistical versus Rule-Based Categorizers
Determining Category Membership
SEQUENCE and PREDICATE_RULE Definitions
Automatic Generation of Categorization Rules Using SAS Text Miner
Differences between Text Clustering and Content Categorization
Challenges in Conducting Sentiment Analysis
Unsupervised versus Supervised Sentiment Classification
SAS Sentiment Analysis Studio Overview
Statistical Models in SAS Sentiment Analysis Studio
Rule-Based Models in SAS Sentiment Analysis Studio
SAS Text Miner and SAS Sentiment Analysis Studio
Case Study 1 Text Mining SUGI/SAS Global Forum Paper Abstracts to Reveal Trends
Instructions for Accessing the Case Study Project
Case Study 2 Automatic Detection of Section Membership for SAS Conference Paper Abstract Submissions
Case Study 3 Features-based Sentiment Analysis of Customer Reviews
Text Mining for Negative App Reviews
Text Mining for Positive App Reviews
Case Study 4 Exploring Injury Data for Root Causal and Association Analysis
Part 2: SAS Enterprise Content Categorization
Case Study 5 Enhancing Predictive Models Using Textual Data
Case Study 6 Opinion Mining of Professional Drivers’ Feedback
Analysis Using SAS® Text Miner
Analysis Using the Text Rule-builder Node
Case Study 7 Information Organization and Access of Enron Emails to Help Investigation
Step-by-Step Software Instruction with Settings/Properties
Case Study 8 Unleashing the Power of Unified Text Analytics to Categorize Call Center Data
Using PROC DS2 for Deployment DEPLOYMENT
Integrating with SAS® Visual Analytics
Case Study 9 Evaluating Health Provider Service Performance Using Textual Responses