Machine generated contents note: 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 .

Bridging the gap between the marketer who must put text analytics to use and the increasingly rarefied community of data analysis experts, this is a 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.