- 159,00 kr
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.
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.