Read The Bestseller Code: Anatomy of a Blockbuster Novel Online Free - Ask most people about massive success in the world of fiction, and you'll typically hear that it's a game of hazy crystals balls. The sales figures of E. L. James or Dan Brown seem to be freakish—random occurrences in an unknowable market. So often we hear that nothing but hype explains their success, but what if there were an algorithm that could reveal a secret DNA of bestsellers, regardless of their genre? What if it knew, just from analyzing the words alone, not just why genre writers like John Grisham and Danielle Steel belong on the lists, but also that authors such as Junot Diaz, Jodi Picoult, and Donna Tartt had tell-tale signs of success all over their pages?
Thanks to Jodie Archer and Matthew Jockers, the algorithm exists, the code has been cracked, and the results bring fresh new insights into how fiction works and why we read. The Bestseller Code offers a new theory for why Fifty Shades of Grey sold so well. It sheds light on the current craze for dark heroines. It reveals which themes tend to sell best. And all with fascinating supporting data taken from a five year study of 20,000 novels. Then there is the hunt for “the one”—the paradigmatic example of bestselling writing according to a computer’s analysis of thousands of points of data. The result is surprising, a bit ironic, and delightfully unorthodox.
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January 06, 2017
I honestly thought I would enjoy this book more than I did. Part of the problem might have been the not-so-secret snobbishness I have when it comes to bestselling novels. There's a little voice in my head that tells me that if a book appeals to the masses, it's probably not going to do much for m...
August 26, 2016
Using a computer algorithm, the authors of this book as the question of whether you can predict whether a novel will be a bestseller or not. Jodie Archer is a former publisher and consultant, while Matthew Jockers is the co-founder of Stanford University’s famed Library Lab. In this work they cla...
September 25, 2016
This book ended up being even more amazing than I expected.
The authors are both literary/publishing experts and have worked on machine learning for years. They fed 5,000 books, published over the past 30 years, to their computer programs. 500 of those were NY Times bestsellers and the rest weren'...
February 09, 2017
The title of this book has it all for me...it's the reason I picked it up in the first place. The idea that blockbuster novels all share some elemental DNA in common is at once exciting and dangerous.
I found that the authors of this book set out to prove their algorithm without giving away too ma...
December 15, 2016
"Recommending a book is not like recommending a health tip or a stock. Recommending a book can be like trying to navigate the unspoken rules and faux pas of a Jane Austen ballroom. The book world comes with considerable baggage."
Who can explain what makes for a best-selling book? What techniques...
June 23, 2017
Very interesting if a bit heavy on the math. If I hit the NYTimes bestsellers list I'll come back and give it the 5th star.
July 09, 2017
I remember reading "The Da Vinci Code" (along with everyone else) and finding the chapter-ending cliffhangers so obvious and annoying, yet I couldn't put it down. There is something to be said for the page-turner, and for writers seeking guidance on how to make their books more palatable to reade...
September 11, 2016
I found this book fascinating reading. The authors wrote a computer programme which could read and analyse books and this is the result. They wanted to see if a computer could predict which books would be best sellers and which wouldn't, A lot of the time it got things right but with some books i...
September 13, 2016
Despite all the efforts of publishers, it has always seemed impossible to predict whether or not a book would be runaway bestseller. This isn't too surprising - it's the kind of thing that is inherently unpredictable because there are simply so many variables involved. Yet a newly published book...
June 30, 2017
Did they get high and write this?
Jesus. This could've been so much better. They had all of this great data and then just dragged the fuck out of every chapter. . .and when the actual date was presented. . .it was fast and in clumps of undecipherable paragraphs.