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

 Author(s)  Gary Miner, John Elder IV, Thomas Hill, Robert Nisbet, Dursun Delen
 Title  Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications
 Edition  1st
 Year  2012
 Publisher  Academic Press
 ISBN  978-0123869791

Table of Contents

Part I Basic Text Mining Principles
1. The History of Text Mining
2. The Seven Practice Areas of Text Analytics
3. Conceptual Foundations of Text Mining and Preprocessing Steps
4. Applications and Use Cases for Text Mining
5. Text Mining Methodology
6. Three Common Text Mining Software Tools

Part II Introduction to the Tutorial and Case Study Section of This Book
AA. CASE STUDY: Using the Social Share of Voice to Predict Events That Are about to Happen
BB. Mining Twitter for Airline Consumer Sentiment
A. Using STATISTICA Text Miner to Monitor and Predict Success of Marketing Campaigns Based on Social Media Data
B. Text Mining Improves Model Performance in Predicting Airplane Flight Accident Outcome
C. Insurance Industry: Text Analytics Adds “Lift” to Predictive Models with STATISTICA Text and Data Miner
D. Analysis of Survey Data for Establishing the “Best Medical Survey Instrument” Using Text Mining
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
F. Using eBay Text for Predicting ATLAS Instrumental Learning
G. Text Mining for Patterns in Children’s Sleep Disorders Using STATISTICA Text Miner
H. Extracting Knowledge from Published Literature Using RapidMiner
I. Text Mining Speech Samples: Can the Speech of Individuals Diagnosed with Schizophrenia Differentiate Them from Unaffected Controls?
J. Text Mining Using STM, CART, and TreeNet from Salford Systems: Analysis of 16,000 iPod Auctions on eBay
K. Predicting Micro Lending Loan Defaults Using SAS Text Miner
L. Opera Lyrics: Text Analytics Compared by the Composer and the Century of CompositiondWagner versus Puccini
M. CASE STUDY: Sentiment-Based Text Analytics to Better Predict Customer Satisfaction and Net Promoter Score Using IBM SPSS Modeler
N. CASE STUDY: Detecting Deception in Text with Freely Available Text and Data Mining Tools
O. Predicting Box Office Success of Motion Pictures with Text Mining
P. A Hands-On Tutorial of Text Mining in PASW: Clustering and Sentiment Analysis Using Tweets from Twitter
Q. A Hands-On Tutorial on Text Mining in SAS: Analysis of Customer Comments for Clustering and Predictive Modeling
R. Scoring Retention and Success of Incoming College Freshmen Using Text Analytics
S. Searching for Relationships in Product Recall Data from the Consumer Product Safety Commission with STATISTICA Text Miner
T. Potential Problems That Can Arise in Text Mining: Example Using NALL Aviation Data
U. Exploring the Unabomber Manifesto Using Text Miner
V. Text Mining PubMed: Extracting Publications on Genes and Genetic Markers Associated with Migraine Headaches from PubMed Abstracts
W. CASE STUDY: The Problem with the Use of Medical Abbreviations by Physicians and Health Care Providers
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
Y. CASE STUDY: Predicting Exposure of Social Messages: The Bin Laden Live Tweeter
Z. The InFLUence Model: Web Crawling, Text Mining, and Predictive Analysis with 2010e2011 Influenza
GuidelinesdCDC, IDSA, WHO, and FMC

Part III Advanced Topics
7. Text Classification and Categorization
8. Prediction in Text Mining: The Data Mining Algorithms of Predictive Analytics
9. Entity Extraction
10. Feature Selection and Dimensionality Reduction
11. Singular Value Decomposition in Text Mining
12. Web Analytics and Web Mining
13. Clustering Words and Documents
14. Leveraging Text Mining in Property and Casualty Insurance
15. Focused Web Crawling
16. The Future of Text and Web Analytics
17. Summary