Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications
Published on 25. January 2012
1000 pages
978-0-12-387011-7 (ISBN)
Description
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
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Gary D. Miner | John Elder | Andrew Fast
Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications
Book
02/2012
Academic Press
€68.09
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Persons
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 (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 (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.
Content
Part I Basic Text Mining Principles1. The History of Text Mining2. The Seven Practice Areas of Text Analytics3. Conceptual Foundations of Text Mining and Preprocessing Steps4. Applications and Use Cases for Text Mining5. Text Mining Methodology6. Three Common Text Mining Software Tools
Part II Introduction to the Tutorial and Case Study Section of This BookAA. CASE STUDY: Using the Social Share of Voice to Predict Events That Are about to HappenBB. Mining Twitter for Airline Consumer SentimentA. Using STATISTICA Text Miner to Monitor and Predict Success of Marketing Campaigns Based on Social Media DataB. Text Mining Improves Model Performance in Predicting Airplane Flight Accident OutcomeC. Insurance Industry: Text Analytics Adds "Lift to Predictive Models with STATISTICA Text and Data MinerD. Analysis of Survey Data for Establishing the "Best Medical Survey Instrument Using Text MiningE. 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 ValidityF. 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 ToolsO. Predicting Box Office Success of Motion Pictures with Text MiningP. 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 ModelingR. Scoring Retention and Success of Incoming College Freshmen Using Text AnalyticsS. Searching for Relationships in Product Recall Data from the Consumer Product Safety Commission with STATISTICA Text MinerT. Potential Problems That Can Arise in Text Mining: Example Using NALL Aviation DataU. Exploring the Unabomber Manifesto Using Text MinerV. 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 ProvidersX. 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 TweeterZ. The InFLUence Model: Web Crawling, Text Mining, and Predictive Analysis with 2010e2011 InfluenzaGuidelinesdCDC, IDSA, WHO, and FMC
Part III Advanced Topics7. Text Classification and Categorization 8. Prediction in Text Mining: The Data Mining Algorithms of Predictive Analytics 9. Entity Extraction10. Feature Selection and Dimensionality Reduction11. Singular Value Decomposition in Text Mining 12. Web Analytics and Web Mining13. Clustering Words and Documents14. Leveraging Text Mining in Property and Casualty Insurance15. Focused Web Crawling16. The Future of Text and Web Analytics
