Practical text analytics : interpreting text and unstructured data for business intelligence /

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Bibliographic Details
Author / Creator:Struhl, Steven M., author.
Imprint:London, UK ; Philadelphia, PA : Kogan Page, 2015.
Description:1 online resource (1 volume) : illustrations.
Language:English
Series:Marketing science series
Marketing science series.
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/13453587
Hidden Bibliographic Details
Varying Form of Title:Interpreting text and unstructured data for business intelligence
ISBN:9780749474027
0749474025
9780749474010
0749474017
Notes:Includes bibliographical references and index.
Description based on print version record.
Summary:"Bridging the gap between the marketer who must put text analytics to use and data analysis experts, Practical Text Analytics is an accessible guide to the many advances in text analytics. It explains the different approaches and methods, their uses, strengths, and weaknesses, in a way that is relevant to marketing professionals. Each chapter includes illustrations and charts, hints and tips, pointers on the tools and techniques, definitions, and case studies/examples. Consultant and researcher Steven Struhl presents the process of text analysis in ways that will help marketers clarify and organize the confusing array of methods, frame the right questions, and apply the results successfully to find meaning in any unstructured data and develop effective new marketing strategies"--
"Bridging the gap between the marketer who must put text analytics to use and the increasingly rarefied community of data analysis experts, Practical Text Analytics is an accessible guide to the many remarkable advances in text analytics that specialists are discussing among themselves. Instead of being a resource for programmers, a book on theory or an introduction on how to use advanced statistical programs, this daily reference resource cuts through the profusion of jargon, evaluating the strengths and weaknesses of various methods and serving as a guide to what is credible in this fast-moving and often confusing field. Practical Text Analytics provides guidance on the application of text analytics for marketing professionals who must interpret the results and apply them in their campaigns. It presents the process of analysis in ways that people who use the data need to see them, helping marketers to clarify and organize confidently the confusing array of methods, frame the right questions and apply the results successfully to find meaning in any unstructured data and develop powerful new marketing strategies. About the series: The Marketing Science series makes difficult topics accessible to marketing students and practitioners by grounding them in business reality. Each book is written by an expert in the field and includes case studies and illustrations so marketers can gain confidence in applying the tools and techniques and commission external research"--
Other form:Print version: Struhl, Steven M. Practical text analytics. London ; Philadelphia : Kogan Page, 2015 9780749474010

MARC

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245 1 0 |a Practical text analytics :  |b interpreting text and unstructured data for business intelligence /  |c Steven Struhl. 
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264 1 |a London, UK ;  |a Philadelphia, PA :  |b Kogan Page,  |c 2015. 
300 |a 1 online resource (1 volume) :  |b illustrations. 
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588 |a Description based on print version record. 
504 |a Includes bibliographical references and index. 
520 |a "Bridging the gap between the marketer who must put text analytics to use and data analysis experts, Practical Text Analytics is an accessible guide to the many advances in text analytics. It explains the different approaches and methods, their uses, strengths, and weaknesses, in a way that is relevant to marketing professionals. Each chapter includes illustrations and charts, hints and tips, pointers on the tools and techniques, definitions, and case studies/examples. Consultant and researcher Steven Struhl presents the process of text analysis in ways that will help marketers clarify and organize the confusing array of methods, frame the right questions, and apply the results successfully to find meaning in any unstructured data and develop effective new marketing strategies"--  |c Provided by publisher. 
520 |a "Bridging the gap between the marketer who must put text analytics to use and the increasingly rarefied community of data analysis experts, Practical Text Analytics is an accessible guide to the many remarkable advances in text analytics that specialists are discussing among themselves. Instead of being a resource for programmers, a book on theory or an introduction on how to use advanced statistical programs, this daily reference resource cuts through the profusion of jargon, evaluating the strengths and weaknesses of various methods and serving as a guide to what is credible in this fast-moving and often confusing field. Practical Text Analytics provides guidance on the application of text analytics for marketing professionals who must interpret the results and apply them in their campaigns. It presents the process of analysis in ways that people who use the data need to see them, helping marketers to clarify and organize confidently the confusing array of methods, frame the right questions and apply the results successfully to find meaning in any unstructured data and develop powerful new marketing strategies. About the series: The Marketing Science series makes difficult topics accessible to marketing students and practitioners by grounding them in business reality. Each book is written by an expert in the field and includes case studies and illustrations so marketers can gain confidence in applying the tools and techniques and commission external research"--  |c Provided by publisher. 
505 0 |a 1 -: Who should read this book? And what do you want to do today?; 2 -: Getting ready: capturing, sorting, sifting, stemming and matching; 3 -: In pictures: Word clouds, wordles and beyond; 4 -: Putting text together: clustering words and documents using words; 5 -: In the mood for sentiment (and counting); 6 -: Predictive models 1: Having words with regressions; 7 -: Predictive models 2: Classifications that grow on trees; 8 -: Predictive models 3: All in the family with Bayes Nets; 9 -: Looking forward and back; A -: Glossary. 
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