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

"The world contains an unimaginably vast amount of digital information which is getting ever vaster ever more rapidly. This makes it possible to do many things that previously could not be done: spot business trends, prevent diseases, combat crime and soon. Managed well, the textual data can be used to unlock new sources of economic value, provide fresh insights into science and hold governments to account. As the Internet expands and our natural capacity to process the unstructured text that it contains diminishes, the value of text mining for information retrieval and search will increase dramatically. This comprehensive professional reference brings together all the information, tools and methods a professional will need to efficiently use text mining applications and statistical analysis. The Handbook of Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications presents a comprehensive how- to reference that shows the user how to conduct text mining and statistically analyze results. In addition to providing an in-depth examination of core text mining and link detection tools, methods and operations, the book examines advanced preprocessing techniques, knowledge representation considerations, and visualization approaches. Finally, the book explores current real-world, mission-critical applications of text mining and link detection using real world example tutorials in such varied fields as corporate, finance, business intelligence, genomics research, and counterterrorism activities"--

List 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

Report

"They ve done it again. From the same industry leaders who brought you the "bible" of data mining comes the definitive, go-to text mining resource. This book empowers you to dig in and seize value, with over two dozen hands-on tutorials that drive an incredible range of applications such as predicting marketing success and detecting customer sentiment, criminal lies, writing authorship, and patient schizophrenia. These step-by-step tutorials immediately place you in the practitioner s driver s seat, executing on text analytics. Beyond this, 17 more chapters cover the latest methods and the leading tools, making this the most comprehensive resource, and earning it a well-deserved place on your desk aside the authors data mining handbook." --Eric Siegel, Ph.D., Founder, Predictive Analytics World, Text Analytics World and Prediction Impact, Inc.
"Of the number of statistics books that are published each year, only a few stand out as really being important, meaning that they positively influence how future research is done in the subject area of the text. I believe that Practical Text Mining is just such a book." --Joseph M. Hilbe, JD, PhD, Arizona State University and Jet Propulsion Laboratory
"When you want real help extracting insight from the mountains of text that you re facing, this is the book to turn to for immediate practical advice." --Karl Rexer, PhD, President, Rexer Analytics, Boston, MA
"The underlying premise is that almost all data in databases takes the form of unstructured text, or summaries of unstructured text, and that historians, marketers, crime investigators, and others need to know how to search that text for meaningful patterns - a very different process than reading. Contributors in a range of fields share their insights and experience with the process. After setting out the principles, they present tutorials and case studies, then move on to advanced topics." --Reference and Research Book News, Inc.
"The authors of Practical Text Mining and Statistical Analysis for Nonstructured Text Data Applications have managed to produce three books in one. First, in 17 chapters they give a friendly yet comprehensive introduction to the huge field of text mining, a field comprising techniques from several different disciplines and a variety of different tasks. Miner and his colleagues have produced a readable overview of the area that is sure to help the practitioner navigate this large and unruly ocean of techniques. Second, the authors provide a comprehensive list and review of both the commercial and free software available to perform most text data mining tasks. Finally, and most importantly, the authors have also provided an amazing collection of tutorials and case studies. The tutorials illustrate various text mining scenarios and paths actually taken by researchers, while the case studies go into even more depth, showing both the methodology used and the business decisions taken based on the analysis. These practical step-by-step guides are impressive not only in the breadth of their applications but in the depth and detail that each case study delivers. The studies are authored by several guest authors in addition to the book authors and are built on real problems with real solutions. These case studies and tutorials alone make the book worth having. I have never seen such a collection of real business problems published in any field, much less in such a new field as text mining. These, together with the explanations in the chapters, should provide the practitioner wishing to get a broad view of the text mining field an invaluable resource for both learning and practice." --Richard De Veaux Professor of Statistics; Dept. of Mathematics and Statistics; Williams Collegeutions

Product details

• Authors: Dursun Delen, IV John Elder, John Elder, John Elder IV, Andrew Fast, Thomas Hill, Gary Miner, Gary D Miner, Gary D. Miner, Gary/ Elder Miner, Ro Nisbet, Robert Nisbet
• Publisher: Academic Press London
• Languages: English
• Product format: Hardback
• Released: 18.02.2012
• EAN: 9780123869791
• ISBN: 978-0-12-386979-1
• Dimensions: 197 mm x 242 mm x 40 mm
• Series: Academic Press
• Subject: Natural sciences, medicine, IT, technology > Mathematics > Probability theory, stochastic theory, mathematical statistics