Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications

Content: 
Front Matter, Page iii
Copyright, Page iv
Dedication, Page v
Endorsements for Practical Text Mining & Statistical Analysis for Non-structured Text Data Applications, Pages xi-xiv
Foreword 1, Pages xv-xvi
Foreword 2, Pages xvii-xviii
Foreword 3, Pages xix-xx
Acknowledgments, Pages xxi-xxii
Preface, Pages xxiii-xxiv
About the Authors, Pages xxv-xxix
Introduction, Pages xxxi-xxxvi
List of Tutorials by Guest Authors, Pages xxxvii-xl
Chapter 1 - The History of Text Mining, Pages 3-27
Chapter 2 - The Seven Practice Areas of Text Analytics, Pages 29-41
Chapter 3 - Conceptual Foundations of Text Mining and Preprocessing Steps, Pages 43-51
Chapter 4 - Applications and Use Cases for Text Mining, Pages 53-72
Chapter 5 - Text Mining Methodology, Pages 73-89
Chapter 6 - Three Common Text Mining Software Tools, Pages 91-121
Introduction, Pages 123-125
Tutorial AA - Case Study: Using the Social Share of Voice to Predict Events That Are about to Happen, Pages 127-131
Tutorial BB - Mining Twitter for Airline Consumer Sentiment, Pages 133-149
Tutorial A - Using STATISTICA Text Miner to Monitor and Predict Success of Marketing Campaigns Based on Social Media Data, Pages 151-180
Tutorial B - Text Mining Improves Model Performance in Predicting Airplane Flight Accident Outcome, Pages 181-201
Tutorial C - Insurance Industry: Text Analytics Adds “Lift” to Predictive Models with STATISTICA Text and Data Miner, Pages 203-231
Tutorial D - Analysis of Survey Data for Establishing the “Best Medical Survey Instrument” Using Text Mining, Pages 233-249
Tutorial E - Analysis of Survey Data for Establishing “Best Medical Survey Instrument” Using Text Mining: Central Asian (Russian Language) Study Tutorial 2: Potential for Constructing Instruments That Have Increased Validity, Pages 251-271
Tutorial F - Using eBay Text for Predicting ATLAS Instrumental Learning, Pages 273-355
Tutorial G - Text Mining for Patterns in Children’s Sleep Disorders Using STATISTICA Text Miner, Pages 357-374
Tutorial H - Extracting Knowledge from Published Literature Using RapidMiner, Pages 375-394
Tutorial I - Text Mining Speech Samples: Can the Speech of Individuals Diagnosed with Schizophrenia Differentiate Them from Unaffected Controls?, Pages 395-412
Tutorial J - Text Mining Using STM™, CART®, and TreeNet® from Salford Systems: Analysis of 16,000 iPod Auctions on eBay, Pages 413-416
Tutorial K - Predicting Micro Lending Loan Defaults Using SAS® Text Miner, Pages 417-455
Tutorial L - Opera Lyrics: Text Analytics Compared by the Composer and the Century of Composition—Wagner versus Puccini, Pages 457-507
Tutorial M - Case Study: Sentiment-Based Text Analytics to Better Predict Customer Satisfaction and Net Promoter® Score Using IBM®SPSS® Modeler, Pages 509-532
Tutorial N - Case Study: Detecting Deception in Text with Freely Available Text and Data Mining Tools, Pages 533-542
Tutorial O - Predicting Box Office Success of Motion Pictures with Text Mining, Pages 543-556
Tutorial P - A Hands-On Tutorial of Text Mining in PASW: Clustering and Sentiment Analysis Using Tweets from Twitter, Pages 557-583
Tutorial Q - A Hands-On Tutorial on Text Mining in SAS®: Analysis of Customer Comments for Clustering and Predictive Modeling, Pages 585-603
Tutorial R - Scoring Retention and Success of Incoming College Freshmen Using Text Analytics, Pages 605-643
Tutorial S - Searching for Relationships in Product Recall Data from the Consumer Product Safety Commission with STATISTICA Text Miner, Pages 645-656
Tutorial T - Potential Problems That Can Arise in Text Mining: Example Using NALL Aviation Data, Pages 657-679
Tutorial U - Exploring the Unabomber Manifesto Using Text Miner, Pages 681-701
Tutorial V - Text Mining PubMed: Extracting Publications on Genes and Genetic Markers Associated with Migraine Headaches from PubMed Abstracts, Pages 703-750
Tutorial W - Case Study: The Problem with the Use of Medical Abbreviations by Physicians and Health Care Providers, Pages 751-772
Tutorial X - Classifying Documents with Respect to “Earnings” and Then Making a Predictive Model for the Target Variable Using Decision Trees, MARSplines, Naïve Bayes Classifier, and K-Nearest Neighbors with STATISTICA Text Miner, Pages 773-796
Tutorial y - Case Study: Predicting Exposure of Social Messages: The Bin Laden Live Tweeter, Pages 797-801
Tutorial Z - The InFLUence Model: Web Crawling, Text Mining, and Predictive Analysis with 2010–2011 Influenza Guidelines—CDC, IDSA, WHO, and FMC, Pages 803-878
Chapter 7 - Text Classification and Categorization, Pages 881-892
Chapter 8 - Prediction in Text Mining: The Data Mining Algorithms of Predictive Analytics, Pages 893-919
Chapter 9 - Entity Extraction, Pages 921-928
Chapter 10 - Feature Selection and Dimensionality Reduction, Pages 929-934
Chapter 11 - Singular Value Decomposition in Text Mining, Pages 935-947
Chapter 12 - Web Analytics and Web Mining, Pages 949-957
Chapter 13 - Clustering Words and Documents, Pages 959-966
Chapter 14 - Leveraging Text Mining in Property and Casualty Insurance, Pages 967-982
Chapter 15 - Focused Web Crawling, Pages 983-989
Chapter 16 - The Future of Text and Web Analytics, Pages 991-1005
Chapter 17 - Summary, Pages 1007-1016
Glossary, Pages 1017-1024
Index, Pages 1025-1046
How to Use the Data Sets and the Text Mining Software on the DVD or on Links for Practical Text Mining, Pages 1047-1053