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Nabokov's Favorite Word Is Mauve: What the Numbers Reveal about the Classics,...
by Blatt, Ben | PB | VeryGood
USD5,45
Aproximadamente4,70 EUR
Estado:
“May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend ”... Más informaciónacerca del estado
En muy buen estado
Libro que se ha leído y que no tiene un aspecto nuevo, pero que está en un estado excelente. No hay desperfectos visibles en la tapa y se incluye sobrecubierta, si procede, para las tapas duras. Todas las páginas están en perfecto estado, sin arrugas ni roturas y no falta ninguna. El texto no está subrayado ni resaltado de forma alguna, y no hay anotaciones en los márgenes. Puede presentar marcas de identificación mínimas en la contraportada o las guardas. Muy poco usado. Consulta el anuncio del vendedor para obtener más información y la descripción de cualquier posible imperfección.
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Ubicado en: Aurora, Illinois, Estados Unidos
Entrega:
Entrega prevista entre el mar. 26 ago. y el mar. 2 sep. a 94104
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N.º de artículo de eBay:144920183146
Última actualización el 19 ago 2025 18:10:26 H.EspVer todas las actualizacionesVer todas las actualizaciones
Características del artículo
- Estado
- En muy buen estado
- Notas del vendedor
- Binding
- Paperback
- Book Title
- Nabokov's Favorite Word Is Mauve
- Weight
- 0 lbs
- Product Group
- Book
- IsTextBook
- No
- ISBN
- 9781501105395
Acerca de este producto
Product Identifiers
Publisher
Simon & Schuster
ISBN-10
1501105396
ISBN-13
9781501105395
eBay Product ID (ePID)
5038432002
Product Key Features
Number of Pages
288 Pages
Publication Name
Nabokov's Favorite Word Is Mauve : What the Numbers Reveal about the Classics, Bestsellers, and Our Own Writing
Language
English
Subject
Authorship, General, Recreations & Games, Linguistics / General
Publication Year
2018
Type
Textbook
Subject Area
Mathematics, Literary Criticism, Language Arts & Disciplines, Humor
Format
Trade Paperback
Dimensions
Item Height
0.7 in
Item Weight
10.6 Oz
Item Length
8.4 in
Item Width
5.5 in
Additional Product Features
Intended Audience
Trade
LCCN
2016-059215
Illustrated
Yes
Synopsis
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 clich s? 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., 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.
LC Classification Number
PN165.B55 2017
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