European University Institute Library

Practical text analytics, interpreting text and unstructured data for business intelligence, Steven Struhl

eng

illustrations

index present

non fiction

Practical text analytics

bibliography

907146659

Steven Struhl

Marketing science series

interpreting text and unstructured data for business intelligence

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.--, Provided by publisher

Preface01 Who should read this book? -- Who should read this book -- Where we find text -- Sense and sensibility in thinking about text -- A few places we will not be going -- Where we will be going from here -- Summary -- References02 Getting ready: capturing, sorting, sifting, stemming and matching -- What we need to do with text -- Ways of corralling words -- Summary -- References03 In pictures: word clouds, wordles and beyond -- Getting words into a picture -- The many types of pictures and their uses -- Clustering words -- Applications, uses and cautions -- Summary -- References04 Putting text together: clustering documents using words -- Where we have been and moving on to documents -- Clustering and classifying documents -- Clustering documents -- Document classification -- Summary -- References05 In the mood for sentiment (and counting) -- Basics of sentiment and counting -- Counting words -- Understanding sentiment -- Summary -- References06 Predictive models 1: having words with regressions -- Understanding predictive models -- Starting from the basics with regression -- Rules of the road for regression -- Divergent roads: regression aims and regression uses -- Practical examples -- Summary -- References07 Predictive models 2: classifications that grow on trees -- Classification trees: understanding an amazing analytical method -- Seeing how trees work, step by step -- CHAID and CART (and CRT, C&RT, QUEST, J48 and others) -- Summary: applications and cautions -- References08 Predictive models 3: all in the family with Bayes Nets -- What are Bayes Nets and how do they compare with other methods? -- Our first example: Bayes Nets linking survey questions and behaviour -- Using a Bayes Net with text -- Bayes Net software: welcome to the thicket -- Summary, conclusions and cautions -- References09 Looking forward and back -- Where we may be going -- What role does text analytics play? -- Summing up: where we have been -- Software and you -- In conclusion -- References Glossary -- Index