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

by Gary D. Miner, John Elder, Andrew Fast, Thomas Hill, Robert Nisbet

Overview

Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications brings together all the information, tools and methods a professional will need to efficiently use text mining applications and statistical analysis.

Winner of a 2012 PROSE Award in Computing and Information Sciences from the Association of American Publishers, this book 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.

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 so on. 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.

• Extensive case studies, most in a tutorial format, allow the reader to 'click through' the example using a software program, thus learning to conduct text mining analyses in the most rapid manner of learning possible
• Numerous examples, tutorials, power points and datasets available via companion website on Elsevierdirect.com
• Glossary of text mining terms provided in the appendix

Product Details

ISBN-13:	9780123870117
Publisher:	Elsevier Science
Publication date:	01/25/2012
Sold by:	Barnes & Noble
Format:	eBook
Pages:	1000
File size:	38 MB
Note:	This product may take a few minutes to download.

About the Author

Dr. Gary Miner PhD received a B.S. from Hamline University, St. Paul, MN, with biology, chemistry, and education majors; an M.S. in zoology and population genetics from the University of Wyoming; and a Ph.D. in biochemical genetics from the University of Kansas as the recipient of a NASA pre-doctoral fellowship. He pursued additional National Institutes of Health postdoctoral studies at the U of Minnesota and U of Iowa eventually becoming immersed in the study of affective disorders and Alzheimer's disease.

In 1985, he and his wife, Dr. Linda Winters-Miner, founded the Familial Alzheimer's Disease Research Foundation, which became a leading force in organizing both local and international scientific meetings, bringing together all the leaders in the field of genetics of Alzheimer's from several countries, resulting in the first major book on the genetics of Alzheimer’s disease. In the mid-1990s, Dr. Miner turned his data analysis interests to the business world, joining the team at StatSoft and deciding to specialize in data mining. He started developing what eventually became the Handbook of Statistical Analysis and Data Mining Applications (co-authored with Drs. Robert A. Nisbet and John Elder), which received the 2009 American Publishers Award for Professional and Scholarly Excellence (PROSE). Their follow-up collaboration, Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications, also received a PROSE award in February of 2013. Gary was also co-author of “Practical Predictive Analytics and Decisioning Systems for Medicine (Academic Press, 2015). Overall, Dr. Miner’s career has focused on medicine and health issues, and the use of data analytics (statistics and predictive analytics) in analyzing medical data to decipher fact from fiction.

Gary has also served as Merit Reviewer for PCORI (Patient Centered Outcomes Research Institute) that awards grants for predictive analytics research into the comparative effectiveness and heterogeneous treatment effects of medical interventions including drugs among different genetic groups of patients; additionally he teaches on-line classes in ‘Introduction to Predictive Analytics’, ‘Text Analytics’, ‘Risk Analytics’, and ‘Healthcare Predictive Analytics’ for the University of California-Irvine. Recently, until ‘official retirement’ 18 months ago, he spent most of his time in his primary role as Senior Analyst-Healthcare Applications Specialist for Dell | Information Management Group, Dell Software (through Dell’s acquisition of StatSoft (www.StatSoft.com) in April 2014). Currently Gary is working on two new short popular books on ‘Healthcare Solutions for the USA’ and ‘Patient-Doctor Genomics Stories’.
Dr. John Elder heads the United States’ leading data mining consulting team, with offices in Charlottesville, Virginia; Washington, D.C.; and Baltimore, Maryland (www.datamininglab.com). Founded in 1995, Elder Research, Inc. focuses on investment, commercial, and security applications of advanced analytics, including text mining, image recognition, process optimization, cross-selling, biometrics, drug efficacy, credit scoring, market sector timing, and fraud detection. John obtained a B.S. and an M.E.E. in electrical engineering from Rice University and a Ph.D. in systems engineering from the University of Virginia, where he’s an adjunct professor teaching Optimization or Data Mining. Prior to 16 years at ERI, he spent five years in aerospace defense consulting, four years heading research at an investment management firm, and two years in Rice's Computational&Applied Mathematics Department.
Dr. Andrew Fast leads research in text mining and social network analysis at Elder Research. Dr. Fast graduated magna cum laude from Bethel University and earned an M.S. and a Ph.D. in computer science from the University of Massachusetts Amherst. There, his research focused on causal data mining and mining complex relational data such as social networks. At ERI, Andrew leads the development of new tools and algorithms for data and text mining for applications of capabilities assessment, fraud detection, and national security. Dr. Fast has published on an array of applications, including detecting securities fraud using the social network among brokers and understanding the structure of criminal and violent groups. Other publications cover modeling peer-to-peer music file sharing networks, understanding how collective classification works, and predicting playoff success of NFL head coaches (work featured on ESPN.com).
Dr. Thomas Hill is Senior Director for Advanced Analytics (Statistica products) in the TIBCO Analytics group. He previously held positions as Executive Director for Analytics at Statistica, within Quest's and at Dell's Information Management Group. He was a Co-founder and Senior Vice President for Analytic Solutions for over 20 years at StatSoft Inc. until the acquisition by Dell in 2014. At StatSoft, he was responsible for building out Statistica into a leading analytics platform. Dr. Hill received his Vordiplom in psychology from Kiel University in Germany, earned an M.S. in industrial psychology and a Ph.D. in psychology from the University of Kansas. He was on the faculty of the University of Tulsa from 1984 to 2009, where he conducted research in cognitive science and taught data analysis and data mining courses. He has received numerous academic grants and awards from the National Science Foundation, the National Institute of Health, the Center for Innovation Management, the Electric Power Research Institute, and other institutions. Over the past 20 years, his team has completed diverse consulting projects with companies from practically all industries in the United States and internationally on identifying and refining effective data mining and predictive modeling / analytics solutions for diverse applications. Dr. Hill has published widely on innovative applications for data mining and predictive analytics. He is the author (with Paul Lewicki, 2005) of Statistics: Methods and Applications, the Electronic Statistics Textbook (a popular on-line resource on statistics and data mining), a co-author of Practical Text Mining and Statistical Analysis for Non-Structured Text Data Applications (2012) and Practical Predictive Analytics and Decisioning Systems for Medicine (2014); he is also a contributing author to the popular Handbook of Statistical Analysis and Data Mining Applications (2009). Dr. Hill also authored numerous patents related to data science, Machine Learning, and specialized applications of of analytics to various domains.
Bob Nisbet, PhD, is a Data Scientist, currently modeling precancerous colon polyp presence with clinical data at the UC-Irvine Medical Center. He has experience in predictive modeling in Telecommunications, Insurance, Credit, Banking. His academic experience includes teaching in Ecology and in Data Science. His industrial experience includes predictive modeling at AT&T, NCR, and FICO. He has worked also in Insurance, Credit, membership organizations (e.g. AAA), Education, and Health Care industries. He retired as an Assistant Vice President of Santa Barbara Bank&Trust in charge of business intelligence reporting and customer relationship management (CRM) modeling.
Dr. Dursun Delen is the William S. Spears Chair in Business Administration and Associate Professor of Management Science and Information Systems in the Spears School of Business at Oklahoma State University (OSU). He received his Ph.D. in industrial engineering and management from OSU in 1997. Prior to his appointment as an assistant professor at OSU in 2001, he worked for a privately owned research and consultancy company, Knowledge Based Systems Inc., in College Station, Texas, as a research scientist for five years, during which he led a number of decision support and other information systems-related research projects funded by federal agencies, including DoD, NASA, NIST and DOE.

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

What People are Saying About This

From the Publisher

In our rapidly moving global marketplace, where information has to be sorted through rapidly to make "good decisions" leading to "actionable and successful" results, this cross-disciplinary approach to text mining provides an easy-to-understand guide that contains step-by-step examples for the professional who needs to learn how to rapidly conduct text mining to incorporate analyzed results into information distillation and thus good decision making

Editorial Reviews

"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 College

"In writing Practical Text Mining and Statistical Analysis for Nonstructured Text Data Applications, the six authors (Miner, Delen, Elder, Fast, Hill, and Nisbet) accepted the daunting task of creating a cohesive operational framework from the disparate aspects and activities of text mining, an emerging field that they appropriately describe as the "Wild West" of data mining. Tapping into their unique expertise and applying a wide cross-application lens, they have succeeded in their mission. Rather than listing the facets of text mining simply as independent academic topics of discussion, the book leans much more to the practical, presenting a conceptual road map to assist users in correlating articulated text mining techniques to categories of actual commonly observed business needs. To finish out the job, summaries for some of the most prevalent commercial text mining solutions are included, along with examples. In this way, the authors have uniquely presented a text mining resource with value to readers across that breadth of business applications." —Gerard Britton, J.D. V.P., GRC Analytics, Opera Solutions LLC

"Text Mining is one of those phrases people throw around as though it describes something singular. As the authors of Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications show us, nothing could be further from the truth. There is a rich, diverse ecosystem of text mining approaches and technologies available. Readers of this book will discover a myriad of ways to use these text mining approaches to understand and improve their business. Because the authors are a practical bunch the book is full of examples and tutorials that use every approach, multiple commercial and open source tools, and that show the power and trade-offs each involves. The case studies are worked through in detail by the authors so you can see exactly how things would be done and learn how to apply it to your own problems. If you are interested in text mining, and you should be, this book will give you a perspective that is broad, deep and approachable." —James Taylor CEO Decision Management Solutions

From the Publisher