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Nabokov's Favorite Word Is Mauve

And Other Experiments in Literature

ebook
1 of 1 copy available
1 of 1 copy available
Data meets literature in this "enlightening" (The Wall Street Journal), "brilliant" (The Boston Globe), "Nate Silver-esque" (O, The Oprah Magazine) look at what the numbers have to say about our favorite authors and their masterpieces.
There's a famous piece of writing advice—offered by Ernest Hemingway, Stephen King, and myriad writers in between—not to use -ly adverbs like "quickly" or "angrily." It sounds like solid advice, but can we actually test it? If we were to count all the -ly adverbs these authors used in their careers, do they follow their own advice? What's more, do great books in general—the classics and the bestsellers—share this trait?

In the age of big data we can answer questions like these in the blink of an eye. In Nabokov's Favorite Word Is Mauve, a "literary detective story: fast-paced, thought-provoking, and intriguing" (Brian Christian, coauthor of Algorithms to Live By), statistician and journalist Ben Blatt explores the wealth of fun findings that can be discovered by using text and data analysis. He assembles a database of thousands of books and hundreds of millions of words, and then he asks the questions that have intrigued book lovers for generations: What are our favorite authors' favorite words? Do men and women write differently? Which bestselling writer uses the most clichés? What makes a great opening sentence? And which writerly advice is worth following or ignoring?

All of Blatt's investigations and experiments are original, conducted himself, and no math knowledge is needed to enjoy the book. On every page, there are new and eye-opening findings. By the end, you will have a newfound appreciation of your favorite authors and also come away with a fresh perspective on your own writing. "Blatt's new book reveals surprising literary secrets" (Entertainment Weekly) and casts an x-ray through literature, allowing us to see both the patterns that hold it together and the brilliant flourishes that allow it to spring to life.
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  • Reviews

    • Publisher's Weekly

      January 16, 2017
      In this diverting if lightweight work, statistician Blatt (coauthor of I Don’t Care If We Never Get Back) applies data analysis techniques to the work of hundreds of authors, from Jane Austen to E.L. James, to extract insights into literary art and human psychology. Opening with the dramatic story of 1960s researchers who used word frequency techniques to solve the Federalist Papers’ authorship, the book never follows up on the promise of comparably exciting or substantial findings. Blatt applies his techniques to look at topics such as adverb usage, the relationship between word choice and gender, and trends in writing complexity. After quick, clear, but cursory descriptions of methods, Blatt details creative visualizations (charts and graphs are included) and findings, but limits the conclusions that can be drawn (“Trying to draw too much meaning out of these findings is a bit like reading tea leaves”). This leaves the reader with the feeling of having witnessed engaging parlor tricks instead of scholarly inquiry. But parlor tricks are fun, and so is this book. Blatt provides amiable and intelligent narration, and literature enthusiasts will enjoy the hypotheses he poses and his imaginative methods. Agent: Jacqueline Ko, Wylie Agency.

    • Kirkus

      January 15, 2017
      Literary criticism by the numbers.Writers write--and write and write. In fact, notes former Slate staffer Blatt (co-author: I Don't Care if We Never Get Back: 30 Games in 30 Days on the Best Worst Baseball Road Trip Ever, 2014), they write more once they get going than when they started. A useful example is J.K. Rowling, whose first Harry Potter book came in at 78,000 words--but who wrote a follow-up three times as long. "If the unknown Rowling had written an 870-page version of the first book in 1997," writes the author, "it would likely have had a much harder time getting published (and getting readers to pick it up)." We are able to know things such as book inflation by applying techniques of big data to the corpus of literature. In Blatt's opening examples, the discussion centers on adverbs, which writers such as Stephen King and Ernest Hemingway have scorned. By doing part-of-speech searches of whole books or even just looking for words that end in -ly (only one class of adverb, as Blatt notes), we can see that those two authors didn't always practice what they preached--and again, that Hemingway's early, harder-worked books were leaner than his later ones, True at First Light being almost twice as adverbial as The Sun Also Rises. One takeaway for writers: "The best books--the greats of the greats--do use a lower rate of -ly adverbs." Statistical approaches to literature have sometimes produced barren results, but Blatt has obvious fun poking around in the stacks, conducting literary experiments that sometimes turn into object lessons: if you want to write like a Brit, use "brilliant," but not too much, lest you sound like an American trying to sound like a Brit. If you want to avoid ridicule, avoid cliches like "past history." And always avoid opening with the weather--unless you're Danielle Steel. If you want to know how many times Chuck Palahniuk uses the verb "snuff," this is just the thing. Illuminating entertainment for literary readers.

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