Practical Text Mining – Applying Analytics and Modeling

In one comprehensive resource, Practical Text Mining and Statistical Analysis for Non-Structured Text Data Applications provides complete coverage of statistical and analytical concepts, techniques, and applications for text mining. Its step-by-step examples will aid professionals, practitioners, researchers, and advanced students—all those who need to learn how to rapidly distill text information into useful insights and actions for good decision making. This thorough reference reveals an in-depth examination of core text mining concepts, tools, and operations, and explains advanced techniques for pre-processing, knowledge representation, and visualization. Twenty-eight tutorials demonstrate realworld, mission-critical applications of text mining in such fields as insurance, finance, fraud detection, counter-terrorism, business intelligence, and genomics.

Only recently has it become practical to begin to tap vast stores of text data for their valuable information. This comprehensive professional reference brings together all the techniques, tools, and methods a professional will need to efficiently use text mining applications. Extensive case studies and tutorials show how to use leading tools to solve real problems in varied fields from corporate finance to business intelligence, and genomics research to counterterrorism. The 1,000-page Handbook divides the field of text mining into seven Practice Areas, defined by the type of text data you have and your goal, and then shows how to accomplish useful tasks in each. Dozens of tutorials, illustrations, and real-world examples make it a tremendous time saver for practitioners seeking to create text-driven solutions.

The book was awarded the 2012 American Publishers PROSE (Professional and Scholarly Excellence) award for Computing and Information Sciences. The PROSE awards annually recognize the very best in professional and scholarly publishing.

Co-Authored by Andrew Fast and John Elder, along with Drs. Gary Miner, Dursun Delen, Thomas Hill, and Robert Nisbet. Click here for a two-page brochure about the book. Click here to view a sample chapter from the book.

"...the definitive, go-to text mining resource."
Eric Siegel, PhD