Practical Text Mining and Statistical Analysis for Non-Structured Text Data Applications - Couverture rigide

Miner, Gary D.; Elder, John; Fast, Andrew; Hill, Thomas; Nisbet, Robert; Delen, Dursun

Couverture rigide

ISBN 10 : 012386979X ISBN 13 : 9780123869791

Editeur : Academic Press Inc, 2012

Synopsis

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. This book examines advanced preprocessing techniques, knowledge representation considerations, and visualization approaches. Full description

A propos du livre

Revue de presse

"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

Biographie de l'auteur

Dr. Gary Miner 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’.

Détails de l'édition

• Éditeur: Academic Press Inc
• Date d'édition: 2012
• ISBN 10: 012386979X
• ISBN 13: 9780123869791
• Reliure: Relié
• Langue: anglais
• Nombre de pages: 1000