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The Bestseller Code

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3.80  ·  Rating details ·  1,011 ratings  ·  244 reviews
What 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 c
...more
Hardcover, 242 pages
Published September 13th 2016 by Allen Lane
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Melki
Jan 06, 2017 rated it liked it  ·  review of another edition
Recommended to Melki by: George Jankovic
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 me. And, in most cases, that's true. I don't very often read titles that make the lists, and when I do, it's usually by accident, or if the book has been chosen by my book club. I've never read anything b ...more
Spencer Orey
There's some good advice in here, even if it mostly feels kind of icky.

Mostly it's a commercial for the services the authors offer.

But I did learn a few things! So that's good!

I guess someday if (when???) I get a seven-figure advance and become the new JK Rowling, I'll change this to five stars. Ha.
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George Jankovic
Sep 07, 2016 rated it it was amazing  ·  review of another edition
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't. They had programs that analyzed, for each book, the themes and topics, ups and downs of the plot, characters and the style. They had an in-sample--10% of bestsellers and 10% of non-bestsellers--that w
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Sebastien Castell
There's an observation that sometimes goes around about how you only need to read the fourth chapter of any given business book. The first is an introduction, the second is about how everything you thought you knew about the subject was wrong, the third is the miraculous tale of how the authors came up with this new secret answer, and the fourth is the actual content. After that it goes into testimonial-style case studies and other rather dull stuff. So, the fourth chapter, or sometimes I've hea ...more
Susan
Aug 26, 2016 rated it liked it
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 claim they can discover a bestseller and analyse 20.000 novels to demonstrate this.

Subtitled, “Anatomy of the Blockbuster Novel,” this book attempts to analyse novels from the points of view of theme, plot,
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Eldon Farrell
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 many of the intricate details (likely proprietary information) and for the most part made their case in a concise and believable manner.

For the most part.

I honestly would've liked to have seen more actual
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Christine Zibas

"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 do best-selling authors employ that makes their works so desirable compared with the majority of authors who struggle for readership? Do those who write literary classics differ so much from those who ap
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Gretchen Rubin
The authors analyze the elements of bestselling novels, and arrive at some surprising conclusions. Fascinating.
Kenya Wright
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.

Great Discoveries.
Horrific Presentation.
And suck ass drag-on writing.
...more
Damaskcat
Sep 09, 2016 rated it it was amazing
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 it was completely wrong - stating that a book was unlikely to be a best seller when it was actually a blockbuster.

I thought it was interesting that a computer could tell whether it was a man or a woman w
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Max Christian Hansen
Novelists: stay at your keyboards.

As a writer, the one resource you can't afford to give away is your time. Therefore avoid this book.

A book that presents statistical analysis should be edited by someone with literary knowledge and by someone with quant knowledge. It may have to be two different people. This book had neither.
Edward Tufte could write an entire volume about bad presentation practices, using only this one book to provide examples of what not to do with statistics.

But it's not j
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Brian Clegg
Sep 13, 2016 rated it really liked it
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 suggests it is possible to do just that. Are the authors crazed or brilliant? Neither, really. They have put together a mechanism based on computerised text analysis that is good at spotting bestsellers ...more
Zora
Mar 11, 2017 rated it did not like it  ·  review of another edition
Ignore all the reviews by novelists sucking up to the authors of this for professional reasons, and what do you have? A bad book with little to reveal.

The old publishing industry, no doubt shamed by the finally accurate raw data Amazon lets anyone see and Author Earnings's incisive analysis of it, is finally trying to do smart analysis after years of guessing very badly. However, here, they largely failed.

Their data points were, if you'll excuse the term, squishy for the most part, had to be in
...more
Q.T. Pi
Is it possible to sort of guess everything the book is going to say, not be surprised by any of its figures, and still feel like you learned something? I feel like I was just told a lot of information I sort of already knew but now that I actually saw it in writing it's actually sticking like I actually feel like I learned it rather than came to it on intuition.

What I found most interesting about this book was the algorithm. It actually quantified success and gave unbiased responses to books th
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Bridget Flynn
Aug 02, 2016 rated it it was amazing  ·  review of another edition
Shelves: non-fiction-misc
When you’re in the middle of devouring Nora Roberts’ latest love story, or admiring the crazy cunning of Amy Dunne in “Gone Girl”, do you ever stop to think, “Gah, why is this book SO GOOD?”

I do, all the time. My theories are vague, and in the past I’ve left it as: the process of writing is a mystery to the masses and an alchemical process for the writer, and no one will ever know what makes a best-seller a best-seller. I enjoy the vagueness. It feels romantic and rebellious in an era where comp
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Mac
Oct 10, 2016 rated it it was ok  ·  review of another edition
The Bestseller Code alternates between pop nonfiction and an academic treatise, which makes sense given the book has co-authors, one a writer and the other a college professor. As a result, my reading experience alternated between enjoying the big ideas and tolerating the science (where admittedly I skimmed the surface). I have no problem with science, but here it seemed a bit dry and well, too academic.

The Bestseller Code had me thinking about writing so I returned to Elmore Leonard's 10 Rules
...more
Andrew Rhomberg
Sep 16, 2016 rated it really liked it  ·  review of another edition
The upcoming book The Bestseller Code is getting a great deal of buzz, forcing many of us to ask the question, Can one genuinely predict what kind of book will become a New York Times bestseller (typically considered the most prestigious bestseller list)?

The promise of a formula for predicting a bestseller is getting many in the publishing industry and those who write about books excited, or at least curious. Several journalists contacted me for an opinion about the book because of my background
...more
Pritesh
Dec 16, 2016 rated it really liked it  ·  review of another edition
Interesting analysis of bestsellers. The authors use textual analysis and machine learning to identify features that differentiate bestsellers from non-bestsellers with greater confidence. For example, bestsellers have a few topics (2-3) that make up 40% of the book. The book also identifies 7 types of plot lines that all bestsellers in their corpus (in the thousands) tend to fall into. The use of sentiment analysis to arrive at this conclusion is very clever! Similar their analysis of the recen ...more
C.L. Lynch
Oct 21, 2016 rated it really liked it  ·  review of another edition
I found this book fascinating but I would have liked twice as much detail and information! I gobbled it up in two nights and came out wanting more. The book involves a lot of literary essay, using the numbers from this algorithm of theirs to back it up. I liked it a lot, but I want more details! More numbers! It's really interesting and I feel like they gave us only a quick peek beneath the surface. ...more
Kirsten McKenzie
I found the insights quite fascinating. More from an interest point of view, than from an author's point of view. Mind you some of their conclusions can easily be incorporated into your writing - type of pet your fictional family should own, where they live etc.
Like any piece of writing advice, read it, and take away from it what you think you can use. Its all knowledge, some is more useful than others.
I think this would be a useful read for a new author.
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Raluca
More like a 2.5/5.

I loved the premise of this book - using algorithms to discover patterns that bestselling books share - but the actual execution was lackluster, dragging on and on and on.

Around half of this book is about how the algorithm works and what it promises to do. I don't mind a few technical details here and there, provided they also deliver what they intend to do, but the delivery part was meh. Face it, as a reader, your incentive for buying this book was the results the algorithm pr
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Carolyn
May 26, 2018 rated it it was ok  ·  review of another edition
I read this only because it is for a bookclub and I managed to find a copy for $7.00. I will admit it did interest that social science geek that I am - I was a sociology and psychology major in university, but beyond that... it was on the drier side.
It was not all bad or boring. There were some interesting points, but seriously, having a computer generate the probability of a novel getting on the New York Times Bestseller list.
The post script talked about several programs that have tried to wri
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Lynn Raye Harris
Interesting analysis!
Stephen Whitehead
You can’t argue with statistics. Ask a computer to crunch some numbers and it never gets them wrong which is why the application of computer science to that most subjective of considerations, what makes a best-selling novel, is so intriguing. It intrigued two busy brains at Stanford University for seven years and their conclusions in The Bestseller Code are going to be required reading for the book trade, writers, aspiring writers and anyone else who loves books. Of course, a computer can only t ...more
Stephen Whitehead
Aug 03, 2016 rated it it was amazing
You can’t argue with statistics. Ask a computer to crunch some numbers and it never gets them wrong which is why the application of computer science to that most subjective of considerations, what makes a best-selling novel, is so intriguing. It intrigued two busy brains at Stanford University for seven years and their conclusions in The Bestseller Code are going to be required reading for the book trade, writers, aspiring writers and anyone else who loves books. Of course, a computer can only t ...more
Jennifer Bradbury
Aug 02, 2016 rated it it was amazing
I was lucky enough to read this book prior to its publication.
For all aspiring authors it is a must, for all those people with an interest in what makes a best seller or in other words a book we will enjoy reading it is truly fascinating and beautifully written.It opens up a world of new yet somehow obvious ideas and explanations as to why some books just work and others do not.
To a layman like myself with only the basic understanding of algorithm's It is made simple with the use of graphs, I fo
...more
Veronica Moss
This was an interesting buddy read with peeps at Goodreads. The authors discuss their development of an algorithm that can predict commercial success for works of fiction. There really should be algorithms for everything ... the entertainment industry would benefit from machine learning for certain. Much of what the authors discovered was not mind-blowing: readers like a fast-moving plot, prefer books with a limited scope of topics and enjoy strong main characters - but there were some interesti ...more
Ksenia Anske
Aug 11, 2017 rated it really liked it  ·  review of another edition
Shelves: favorites
Some books I read and feverishly take pictures of certain pages, in fear of forgetting some valuable wisdoms. This book I think I photographed a gazillion times (took pics of a gazillion pages, I mean). It confirmed what I've been reading over the last five years in the books on the craft of writing, only this time seen through the eyes of the computer that has analyzed bestsellers and extracted data on what sells and what doesn't and why. A good read to refresh your striving for excellence and, ...more
Spencer Borup
I picked this up expecting it to be an explanation of why James Patterson and Nora Roberts and 50 Shades of Grey and Gone Girl and The Martian were astronomical hits on the New York Times best seller list.

But what I found (about halfway through) was one of the greatest books on the writing craft. The Bestseller Code comes from a completely different, refreshing approach, offers empirical data to back up its claims, and cuts the bullshit: no opinions, just facts.

Incredible. If you are a writer or
...more
Mugren Ohaly
Jan 26, 2017 rated it liked it
Shelves: 2017
An interesting read.

It's a shame that big parts of it were dull. It could've been written using half the word-count.
...more
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topics  posts  views  last activity   
Advice for an amazing novel still in the works 1 3 Jul 05, 2017 07:01PM  
Result of data mining best sellers 5 1 Dec 20, 2016 09:02PM  
Bestseller first lines 1 4 Oct 13, 2016 08:39AM  

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Jodie Archer spent her childhood hiding in the changing rooms of the clothing stores her mother managed: she would pile sweaters all over the floor to look as though she were putting together an outfit, and would sit for the 8 hours the shops were open and read books in any genre she was allowed. At this age, she decided the only sensible profession in the world was to be a writer, but sensible pe ...more

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