LEADER 04799nam 2200625 450 001 9910131514403321 005 20230807220957.0 010 $a9780749474027 010 $a0-7494-7402-5 010 $a0-7494-7401-7 035 $a(CKB)3710000000442150 035 $a(EBL)2081435 035 $a(OCoLC)913562930 035 $a(SSID)ssj0001517709 035 $a(PQKBManifestationID)11895226 035 $a(PQKBTitleCode)TC0001517709 035 $a(PQKBWorkID)11504211 035 $a(PQKB)10526016 035 $a(MiAaPQ)EBC2081435 035 $a(EXLCZ)993710000000442150 100 $a20150715h20152015 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aPractical text analytics $einterpreting text and unstructured data for business intelligence /$fSteven Struhl 210 1$aLondon, England ;$aPhiladelphia, Pennsylvania ;$aNew Delhi, India :$cKogan Page,$d2015. 210 4$dİ2015 215 $a1 online resource (272 p.) 225 0 $aMarketing Science Series 300 $aDescription based upon print version of record. 311 08$aPrint version: Struhl, Steven M. Practical text analytics : interpreting text and unstructured data for business intelligence. London, England ; Philadelphia, Pennsylvania : New Delhi, India : Kogan Page, c2015 xiv, 258 pages Marketing Science Series 9780749474010 2015016005 320 $aIncludes bibliographical references and index. 327 $aMachine 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 . 330 $aBridging the gap between the marketer who must put text analytics to use and the increasingly rarefied community of data analysis experts, Practical Text Analytics is an accessible guide to the many remarkable advances in text analytics that specialists are discussing among themselves. Instead of being a resource for programmers, a book on theory or an introduction on how to use advanced statistical programs, this daily reference resource cuts through the profusion of jargon, evaluating the strengths and weaknesses of various methods and serving as a guide to what is credible in this fast-movi 410 0$aMarketing Science 606 $aMarketing$xData processing 606 $aBig data 606 $aBusiness intelligence 606 $aMarketing research 615 0$aMarketing$xData processing. 615 0$aBig data. 615 0$aBusiness intelligence. 615 0$aMarketing research. 676 $a658.4/72 686 $aBUS043060$aCOM021030$aBUS043000$2bisacsh 700 $aStruhl$b Steven M.$0117422 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910131514403321 996 $aPractical text analytics$92891679 997 $aUNINA