- Series: Quantitative Methods in the Humanities and Social Sciences
- Hardcover: 194 pages
- Publisher: Springer; 2014 edition (June 11, 2014)
- Language: English
- ISBN-10: 3319031635
- ISBN-13: 978-3319031637
- Product Dimensions: 6.1 x 0.5 x 9.2 inches
- Shipping Weight: 1.1 pounds (View shipping rates and policies)
- Average Customer Review: 11 customer reviews
- Amazon Best Sellers Rank: #482,440 in Books (See Top 100 in Books)
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Text Analysis with R for Students of Literature (Quantitative Methods in the Humanities and Social Sciences) 2014th Edition
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“The aim of this book is … to give the Literature students just the most basic tools needed to do some relatively straightforward textual analysis. … Even though this is primarily a book intended for literature students, I would actually strongly recommend it to anyone interested in text mining, text analysis and natural language processing. It is a very gentle and approachable introduction to the whole world of textual analysis.” (Bojan Tunguz, tunguzreview.com, July, 2015)
“This is a well written book on the topic of Text Analysis. There is enough information to give you a good start using R. Followed by easy to understand details about text analysis. … This is a good book to have if you are doing text analysis.” (Mary Anne, Cats and Dogs with Data, maryannedata.com, August, 2014)
“A remarkably well-crafted book that will allow students to get a quick start and progress toward quite sophisticated text mining tasks. … exercises provided at the end of each chapter, with solutions at the end of the book, should serve well to help students solidify their knowledge and gain more confidence in their text mining skills. … a great addition to the libraries of digital humanists and natural language enthusiasts who wish to expand their programming literacy … .” (Denilson Barbosa, Computing Reviews, August, 2014)
"I can't think of a more qualified person to guide readers through powerful R techniques for text analysis. While extremely useful for people studying literature, these techniques can be also used by anybody working with texts. Even if you simply want to understand how companies and data scientists are analyzing all kinds of texts, go through this book." (Lev Manovich, Department of Computer Science, The Graduate Center, City University of New York & author of The Language of New Media)
"The open source programming language R has become one of the most central statistical and analytical tool in many sciences. While it has already been used in linguistic applications, this book is the first to discuss the application of (corpus-linguistic and other) methods with R in the context of literary studies. The author covers a wide range of descriptive, analytical, and exploratory methods beautifully and in detail in a book that will appeal to a wide and diverse audience of both students and seasoned researchers from literary studies, linguistic computing, and the digital humanities more generally." (Stefan Th. Gries, Department of Linguistics, University of California, Santa Barbara & author of Quantitative corpus linguistics with R: A Practical Introduction)
"This book does a great service for literary scholars interested in computational approaches to text analysis, giving them ready access to powerful methods for exploring patterns and relationships across large quantities of text. Its clear and lucid explanations will also make it an easy textbook to teach from, especially for instructors with prior background who can then use it as a stepping stone to introducing more complex methods. Amateurs and those with little programming background will find it imminently accessible." (Hoyt Long, Department of East Asian Languages and Civilizations, University of Chicago)
"Through my work as an epidemiologist, I encounter electronic health records in an unstructured form (i.e. text), and Text Analysis with R covers many of the initial steps for studying these records. The book is very accessible; it provides a straightforward introduction to manipulating text information without presuming a background in programming or a familiarity with the jargon used in this field. I also appreciated Jockers' thoughtful inclusion of supplemental explanations and information in footnotes throughout the book. For example, text analysis often involves the use of "regular expressions"; a footnote concisely explains wildcard and escape characters and this explanation spared me a fair bit of confusion in my own work. Although I am not a "student of literature", I thought the book contained many generalizable and expertly-taught lessons that make it a valuable introduction to manipulating and analyzing text." (Matthew Maenner, Ph.D.)
"This book is a worthy introduction to computational text analysis, and it fills an important gap in the literature. It’s very accessible and contains plenty of interesting examples and real applications, which have been collected and crafted over the many years the author taught text analysis to undergraduate and graduate students. Although it focuses on the study of literature, I would highly recommend this book to students in business administration and related fields." (Joao Quariguasi Frota Neto, School of Management, University of Bath)
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Mario A. Martinez, Ph.D. (Curriculum and Instruction)
Though I have quite a bit of experience with HTML, CSS, and XML, but I had no programming experience prior to reading this book aside from some classes in high school. I knew from my experience of learning HTML that what I needed were (1) some clear explanations of basic concepts and (2) some sample scripts to tinker with and adapt to my own uses. Point (2) is crucial: it's very hard to make scrips from scratch, but I find that once I see a script that's doing something similar to what I want to do, it's relatively easy to customize it to my needs. But, as I know from a few failed attempts to pick up programming from MOOCs and For Dummies books, I also knew that I needed instruction focused specifically on literary analysis. I wasn't interested in learning to program for its own sake; I just wanted to be able to carry out my particular literary research project, which required me to know how to do some programming.
Text Analysis in R was fantastic on all counts: the focus is specifically literary, the explanations are very lucid; and the example scripts are close enough to what I needed to do that I was able to customize them. (The chapter on Topic Modelling was of course particularly useful to me.)
I read the book sequentially in about a month, doing all the practice exercises along the way. (I read it on the bus on my commute... about 6 hours/week.) By the time I finished, I was ready to fly on my own. In the following weeks, I was able to customize the scripts to my own needs. The modifications were sometimes very significant; I was somewhat amazed at my own progress, I must say. I was able to make the system I initially thought I would need to hire a programmer to develop: a web-based interactive topic modelling browser, in which you enter your own keywords, and the system return the topics in which that word is most likely to appear, showing a word cloud, distribution chart, and list of top texts for each topic). I recently presented my work at a major conference in my field. It went over very well -- and since I did the programming myself, I was able to field technical as well as literary questions.
I feel immensely empowered by this book. I have also definitely caught the programming bug. I can't leave R alone now.
One final note: some of my programmer friends have turned up their noses at the idea that I would learn R rather than Python, which they all seem to prefer. I chose this book not because I thought R was the right language for me, but just because it seemed like it was focused on the sorts of TASKS I was interested in. Having gone through the book, I'm now fully convinced that R is an excellent language for literary analysis. The ease of outputting tables and charts -- and the ease of performing statistical calculations -- are particularly welcome features of R.