- Print ISBN 9780123869791
- Electronic ISBN 9780123870117
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
In one comprehensive resource, this book provides complete coverage of statistical analysis and text mining applications to aid professionals, practitioners, researchers and upper level undergraduate and graduate students for those who need to learn how to rapidly do text mining to incorporate into information distillation and thus good decision making.
Endorsements for Practical Text Mining & Statistical Analysis for Non-structured Text Data Applications
About the Authors
Building the Workshop Manual
The Structure of this Book
Part I: Basic Text Mining Principles
Part II: Tutorials
Part III: Advanced Topics
Why Did We Write This Book?
What Are the Benefits of Text Mining?
List of Tutorials by Guest Authors
Part I: Basic Text Mining Principles
Chapter 1. The History of Text Mining
The Roots of Text Mining: Information Retrieval, Extraction, and Summarization
Information Extraction and Modern Text Mining
Major Innovations in Text Mining since 2000
The Development of Enabling Technology in Text Mining
Emerging Applications in Text Mining
Sentiment Analysis and Opinion Mining
IBM’s Watson: An “Intelligent” Text Mining Machine?
Chapter 2. The Seven Practice Areas of Text Analytics
What is Text Mining?
The Seven Practice Areas of Text Analytics
Five Questions for Finding the Right Practice Area
The Seven Practice Areas in Depth
Interactions between the Practice Areas
Scope of This Book
Chapter 3. Conceptual Foundations of Text Mining and Preprocessing Steps
Syntax versus Semantics
The Generalized Vector-Space Model
Creating Vectors from Processed Text
Chapter 4. Applications
"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." —