MARC record from Internet Archive

LEADER: 02170cam 2200457Mi 4500
001 ocn973899168
003 OCoLC
005 20201126113248.0
008 170227s2017 enk g 000 0 eng d
040 $aYDX$beng$erda$cYDX$dERD$dOCLCO$dQGJ$dAUPTL$dOCLCF$dUKMGB$dNTE
015 $aGBB774445$2bnb
016 7 $a018329471$2Uk
019 $a974034812$a974328358$a974469775$a974554912$a974682387
020 $a0141982489$q(paperback)
020 $a9780141982489$q(paperback)
035 $a(OCoLC)973899168$z(OCoLC)974034812$z(OCoLC)974328358$z(OCoLC)974469775$z(OCoLC)974554912$z(OCoLC)974682387
082 04 $a070.5$223
082 04 $a028$223
100 1 $aArcher, Jodie,$eauthor.
245 14 $aThe bestseller code /$cJodie Archer and Matthew L. Jockers.
264 1 $a[London] :$bPenguin Books,$c[2017].
264 4 $c©2016
300 $a242 pages :$billustrations ;$c20 cm.
336 $atext$btxt$2rdacontent
336 $astill image$bsti$2rdacontent
337 $aunmediated$bn$2rdamedia
338 $avolume$bnc$2rdacarrier
520 $aWhat if an algorithm could predict which manuscripts would become mega-bestsellers? 'Girl on the Train'. 'Fifty Shades'. 'The Goldfinch'. Why do some books capture the whole world's attention? What secret DNA do they share? In 'The Bestseller Code', Archer and Jockers boldly claim that blockbuster hits are highly predictable, and they have created the algorithm to prove it. Using cutting-edge text mining techniques, they have developed a model that analyses theme, plot, style and character to explain why some books resonate more than others with readers.
504 $aIncludes bibliographical references.
650 0 $aBest sellers.
650 0 $aBooks and reading.
650 0 $aBest sellers$xMathematical models.
650 7 $aBest sellers.$2fast$0(OCoLC)fst00830872
650 7 $aBooks and reading.$2fast$0(OCoLC)fst00836454
700 1 $aJockers, Matthew Lee,$d1966-$eauthor.
938 $aYBP Library Services$bYANK$n13515387
029 1 $aUNITY$b140516026
029 1 $aAU@$b000060620276
029 1 $aUKMGB$b018329471
994 $aZ0$bP4A
948 $hNO HOLDINGS IN P4A - 56 OTHER HOLDINGS