Macroanalysis: Digital Methods and Literary History

Topics in the Digital Humanities

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.

Genres: Nonfiction, Literary Criticism, Academic, Criticism, Research, Design, Grad School

292 pages, Kindle Edition

First published March 15, 2013

Reviews

[Khanim Garayeva · Rating 5 out of 5]

This work sheds a light on the computational examination of the textual data that enable scholars to articulate new questions about the certain literary databases without close-reading them. The questions asked in this book cover the issues like the historical place of individual texts, authors and genres in relation to a larger literary contexts, the literary production in terms of growth and decline over time or within regions or within demographic groups, literary patterns and lexicons over time or across periods, the cultural and societal influencing the literary style and the evolution of the style, the linkages among authors, texts and genres, the probability of literature’s being evolutionary, of the historical events and literary trends being correlative, of the style’s being nationally determined. The computational analysis given in this book offers Jockers’ perspective on the creation of enormous databases by digitising literary works, the constitution of style, the definition of the nationality of the authors, the identification of the thematic content of certain literary works, the influence of given texts to the creative activity of various authors only by using digital softwares and tools. The study shows, on the basis of the outcomes, how scholars are able to study larger collections to better understand or contextualise individual works within these large collections, to what extent sub-genres reflect the larger genres of which they are a subset, etc.

[Alex · Rating 4 out of 5]

Super geeky book but packed with interesting insights. It's a little hard to read, especially the first few chapters as Jockers makes a quite academic introduction on the subject, the field and focuses most of the examples on Irish literature. Nothing against Irish-American literature, but it turns a little too academic for my taste. Once you survive those two chapters, the rest of the book is wonderful and full of ideas and experiments that can be tried. If you're into digital methods of literary analysis, this book is very much worth reading.

[Catherine · Rating 5 out of 5]

Recommended reading for anyone interested in digital text analysis and literary history. Jockers' writing is clear and accessible, even for those who may have never programmed. He makes compelling arguments for the necessity of moving beyond close reading and provides concise, apt examples of the potential - and shortcomings - of digital text analysis. I haven't enjoyed reading academic work like this in a long time!

[E. · Rating 3 out of 5]

A good, gentle introduction to the field. Delivery is very clear and accessible, but there are some downsides to this clarity. Statistical details (the very thing that makes data-based arguments stronger than gross generalizations) are sacrificed for the elegance of deliverance, and the examples for statistical techniques can come across as a bit puerile and (dangerously) oversimplified for anyone with any kind of statistics background. The author also makes little attempt to *show* how this form of analysis can complement close reading, rather than existing as a parallel form of reading that only trivially borrows some observations and terminologies from the old school.

Now many of this is understandable - the author is writing for someone who is neither familiar with nor believing in this form of "microanalysis". Nonetheless, I personally think the book could have benefited from more rigorous developments on the ideas that it set out to convey. As it is, this book only belongs in the "cool idea" category.

[Vivian Halloran · Rating 5 out of 5]

This was a thoroughly enjoyable read. The prose is lucid, there are ample definitions of key terms, and the overall tone is personable and disarming. The discussion of his work on Irish and Irish American novels was engaging--his anecdotes about the various trials and errors involved in this research made me realize the value of macro analysis as a complement to close reading.

[Humphrey · Rating 3 out of 5]

Accessible and firm argument for macroanalysis in the humanities, but I found it a bit lighter on methodological claims than what I would've personally preferred for a monograph unified by method rather than period/theme.

[Max Lauber · Rating 5 out of 5]

A really introduction to automated textual analysis. Non-technical but conceptually clear, short but offering an overview of numerous approaches (the use of metadata, stylistic analysis, topic modelling and influence between works). Highly recommended!

[Natxo Sorolla · Rating 5 out of 5]

Molt bona lectura. Dirigit a investigadors que treballen en literatura com a introducció a l'anàlisi quantitativa. Molt útil per a formar el marc teòric d'anàlisi macro en literatura, i dona bones pistes sobre la metodologia.

[Jay Jolles · Rating 4 out of 5]

I'm not sold on the macroanalytic approach to literature (and I wasn't years ago when I read Distant Reading). That said, I think this book was shockingly readable for both its subject matter and the fact that it's a monograph.

[Marijana Dragičević · Rating 1 out of 5]

Sve zaključke koje je iznesao već su odavno poznati putem pomnog čitanja koje toliko kritizira. Govori o mega-mogućnostima velikih korpusa i onda se opet usmjeri na zapadnoeuropsku i američku literaturu. Žao mi je vremena, ali morala sam se pročitati jer je dio lietarture na fakultetu.

[Matthew Jockers]

Love it;-)

[GONZA · Rating 4 out of 5]

This book is terrific, immagine a crazy litterate that create a statistical model to verify who were the most important authors in the english language litterature (Austen and Scott) and the most important argument about which authors wrote. Then you can immagine more or less this book, that is not easy at all, but so interesting! Using Latent Dirichlet Allocation the author shows this topoi and the other related to them as if they were a cloud but it's better if you co on his page and try this yourself because I'm not very good at explaining: http://www.matthewjockers.net/macroan...
What is even more good in my opinion is that is only the beginning!

Questo libro è fantastico, immaginatevi un umanista pazzo che crea un modello statistico computerizzato per evidenziare quali siano i più importanti autori in lingua inglese e i temi più utilizzati sempre in questa letteratura. A questo punto avete una vaga idea del libro, che non è affatto semplice ma interessantissimo! Utilizzando un programma creato dall'autore che si chiama Latent Dirichlet Allocation l'autore mostra questi argomenti come se fossero una nuvola, ma siccome io non mi so spiegare è meglio se fate un salto su questa pagina e vi rendete conto da soli: http://www.matthewjockers.net/macroan...
La cosa migliore poi, è che questo è solo l'inizio!

THANKS TO NETGALLEY AND UNIVERITY OF ILLINOIS PRESS FOR THE PREVIEW

[Liz · Rating 4 out of 5]

And now I remember why I wanted to learn R over summer break. This book assumes at least a passing familiarity with Digital Humanities (at least, it assumes that you've heard of it) and is mostly a well-curated and intriguing tour of what the Stanford Literary Lab has been up to over the past five years. Jockers' work is especially useful because he has a very clear idea of why he is doing what he is doing (which does not always come through in the shorter discussions of his work). His focus isn't just on figuring out the tools, but in asking questions about their implementation. What can I do with this and, perhaps, more crucially, how.
I found the defense of DH sections a bit tedious, but I suppose I wasn't the intended audience. My relationship with close reading is not threatened by the advent of Macroanalysis (see the bit about meaning to learn R).
What Jockers has written is, in some ways, a tool for generating interest in macroanalysis. This book is certainly not a how-to guide, but I have a difficult time imagining the kind of person who reads it and does not find herself wanting to try out some of these techniques (and then you get to the last chapter and realize that everything you are interested in is under copyright...).

[Andrew · Rating 4 out of 5]

A refreshingly approachable introduction to the complex world of big data analysis in the humanities. Given that a good chunk of his audience could find programming jargon intimidating, Matthew Jockers does a great job explaining his work and the theory behind it without overwhelming readers. While I would have enjoyed an appendix with more information to help apply his methods, he has also offered Text Analysis with R for Students of Literature for those interested in learning more about the technology.

[John · Rating 4 out of 5]

A surprisingly clear book on using data mining techniques to discover patterns in Victorian Literature. Not sure I can readily accept all the conclusions in the book, but the techniques used are inspiring.

[Stephen Houser · Rating 3 out of 5]

Good introduction to digital humanities and text-mining. Thought provoking in how he "fingerprinted" texts with 40+ style features and 500+ theme features. Not enough detail in the book to reproduce the work, but enough to get a start in the field. Footnotes, footnotes, footnotes...

[James Igoe · Rating 4 out of 5]

Great foray into data mining literature, although at times a bit tedious and redundant. Conceptually useful, for general concepts in data mining, but with no actual coding.