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

Gary Miner 지음

elsevier 2012년 01월 25일 출간

작품소개

책소개

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

목차

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 Mini

출판사 서평

"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 Rese

작가정보

저자(글) Gary Miner