Matthew L. Jockers's Books

The Bestseller Code

Anatomy of the Blockbuster Novel

By: Jodie Archer,Matthew L. Jockers

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The Bestseller Code

Anatomy of the Blockbuster Novel

By: Jodie Archer,Matthew L. Jockers

What if there was an algorithm that could predict which novels become mega-bestsellers? Are books like Dan Brown's The Da Vinci Code and Gillian Flynn's Gone Girl the Gladwellian outliers of publishing? [This book] boldly claims that the New York Times bestsellers in fiction are predictable and that it's possible to know with 97% certainty if a manuscript is likely to hit number one on the list as opposed to numbers two through fifteen. The algorithm does exist; the code has been cracked; the results are in"--

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Page Count

253

Publisher

Macmillan

Published Date

ISBN 10

1250088275

ISBN 13

9781250088277

Becoming Bestsellers: John Grisham and Danielle Steel (Sample from Chapter 2 of THE BESTSELLER CODE)

By: Jodie Archer,Matthew L. Jockers

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Becoming Bestsellers: John Grisham and Danielle Steel (Sample from Chapter 2 of THE BESTSELLER CODE)

By: Jodie Archer,Matthew L. Jockers

This sneak peek teaser - featuring literary giants John Grisham and Danielle Steele - from Chapter 2 of The Bestseller Code, a groundbreaking book about what a computer algorithm can teach us about blockbuster books, stories, and reading, reveals the importance of topic and theme in bestselling fiction according to percentages assigned by what the authors refer to as the “bestseller-ometer.” Although 55,000 novels are published every year, only about 200 hit the lists, a commercial success rate of less than half a percent. When the computer was asked to “blindly” select the most likely bestsellers out of 5,000 books published over the past thirty years based only on theme, it discovered two possible candidates: The Accident by Danielle Steel and The Associate by John Grisham. The computer recognized quantifiable patterns in their seemingly opposite, but undeniably successful writing careers with legal thrillers and romance. In Chapter 2, Archer and Jockers analyze this data and divulge the most and least likely to best sell topics and themes in fiction with a human discussion of the “why” behind these results. The Bestseller Code is a big-idea book about the relationship between creativity and technology. At heart it is a celebration of books for readers and writers—a compelling investigation into how successful writing works.

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253

Publisher

St. Martin's Press

Published Date

ISBN 10

1250137950

ISBN 13

9781250137951

Text Analysis with R

For Students of Literature

By: Matthew L. Jockers,Rosamond Thalken

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Text Analysis with R

For Students of Literature

By: Matthew L. Jockers,Rosamond Thalken

Now in its second edition, Text Analysis with R provides a practical introduction to computational text analysis using the open source programming language R. R is an extremely popular programming language, used throughout the sciences; due to its accessibility, R is now used increasingly in other research areas. In this volume, readers immediately begin working with text, and each chapter examines a new technique or process, allowing readers to obtain a broad exposure to core R procedures and a fundamental understanding of the possibilities of computational text analysis at both the micro and the macro scale. Each chapter builds on its predecessor as readers move from small scale “microanalysis” of single texts to large scale “macroanalysis” of text corpora, and each concludes with a set of practice exercises that reinforce and expand upon the chapter lessons. The book’s focus is on making the technical palatable and making the technical useful and immediately gratifying. Text Analysis with R is written with students and scholars of literature in mind but will be applicable to other humanists and social scientists wishing to extend their methodological toolkit to include quantitative and computational approaches to the study of text. Computation provides access to information in text that readers simply cannot gather using traditional qualitative methods of close reading and human synthesis. This new edition features two new chapters: one that introduces dplyr and tidyr in the context of parsing and analyzing dramatic texts to extract speaker and receiver data, and one on sentiment analysis using the syuzhet package. It is also filled with updated material in every chapter to integrate new developments in the field, current practices in R style, and the use of more efficient algorithms.

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Page Count

283

Publisher

Springer Nature

Published Date

ISBN 10

3030396436

ISBN 13

9783030396435

Macroanalysis

Digital Methods and Literary History

By: Matthew L. Jockers

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Macroanalysis

Digital Methods and Literary History

By: Matthew L. Jockers

In this volume, Matthew L. Jockers introduces readers to large-scale literary computing and the revolutionary potential of macroanalysis--a new approach to the study of the literary record designed for probing the digital-textual world as it exists today, in digital form and in large quantities. Using computational analysis to retrieve key words, phrases, and linguistic patterns across thousands of texts in digital libraries, researchers can draw conclusions based on quantifiable evidence regarding how literary trends are employed over time, across periods, within regions, or within demographic groups, as well as how cultural, historical, and societal linkages may bind individual authors, texts, and genres into an aggregate literary culture. Moving beyond the limitations of literary interpretation based on the "close-reading" of individual works, Jockers describes how this new method of studying large collections of digital material can help us to better understand and contextualize the individual works within those collections.

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Page Count

211

Publisher

University of Illinois Press

Published Date

ISBN 10

025209476X

ISBN 13

9780252094767

Text Analysis with R for Students of Literature

By: Matthew L. Jockers

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Text Analysis with R for Students of Literature

By: Matthew L. Jockers

Text Analysis with R for Students of Literature is written with students and scholars of literature in mind but will be applicable to other humanists and social scientists wishing to extend their methodological tool kit to include quantitative and computational approaches to the study of text. Computation provides access to information in text that we simply cannot gather using traditional qualitative methods of close reading and human synthesis. Text Analysis with R for Students of Literature provides a practical introduction to computational text analysis using the open source programming language R. R is extremely popular throughout the sciences and because of its accessibility, R is now used increasingly in other research areas. Readers begin working with text right away and each chapter works through a new technique or process such that readers gain a broad exposure to core R procedures and a basic understanding of the possibilities of computational text analysis at both the micro and macro scale. Each chapter builds on the previous as readers move from small scale “microanalysis” of single texts to large scale “macroanalysis” of text corpora, and each chapter concludes with a set of practice exercises that reinforce and expand upon the chapter lessons. The book’s focus is on making the technical palatable and making the technical useful and immediately gratifying.

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