Text Analysis With R For Students Of Literature Download

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Sep 11, 2018 Justohidalgo rated it really liked it

Even though I had already played around with R, I really wanted to try it out in a context I am specially fond of, which is text and specifically book text. This book is just what I was looking for.
I already knew about the author from his well-known work The Bestselling code (at least in the publishing world :)).

In this case, the author goes quite technical and explains how to manage and massage ebook data with R. This means that the reader, obviously, needs to understand basic statistics, some

In this case, the author goes quite technical and explains how to manage and massage ebook data with R. This means that the reader, obviously, needs to understand basic statistics, some coding, etc.

The book does not get farther than some clustering, supervised classification and topic modelling using Mallet -which BTW I was unable to run in my laptop due to version conflicts, so I had to use the Java version of Mallet with a different dataset-. But I think this is exactly what a reader like me needed, to start playing around and understanding how pieces work together.

Don't expect fancy results or amazing visualizations. Most of the book focuses on transforming the ebook data/metadata in the appropriate tables, matrices or data frames requires for the magic to happen. Exactly what one expects from a work attempting to be useful.

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Jan 13, 2020 Arlie rated it liked it

Fine introduction to text analysis in r. The author skips some things like stopwords and lemmatizing that I had to find elsewhere. It would have been useful to have guides for using the tm package with different kinds of data, like dataframes. This is a good place to start, but be prepared to look up the rest on stackexchange.

Jul 14, 2015 Bojan Tunguz rated it it was amazing

Our ability to access, process, and analyze large quantities of data has been increasing at a dizzying pace over the last few years. This data-driven revolution is fundamentally changing many professional and academic fields. Many people, especially the long-term practitioners in humanities and similar disciplines, find this change worrying, and in many ways exactly contrary to the spirit of these disciplines. Pouring over long and demanding texts, while internalizing them and becoming personall Our ability to access, process, and analyze large quantities of data has been increasing at a dizzying pace over the last few years. This data-driven revolution is fundamentally changing many professional and academic fields. Many people, especially the long-term practitioners in humanities and similar disciplines, find this change worrying, and in many ways exactly contrary to the spirit of these disciplines. Pouring over long and demanding texts, while internalizing them and becoming personally immersed in them, seems to be at the very core of what these disciplines are all about. And yet, as both a lover of humanities and a die-hard techy, I find this latest development incredibly exciting.

The title of this short book makes it eminently clear who the intended audience is: students of literature who are interested in using R for textual analysis. R is a very powerful programming language used for statistical analysis. Textual analysis is a very prominent aspect of modern data science, so there are many well-known and established tools and techniques that can help one with this task. However, the aim of this book is neither to teach R or programming, but to give the Literature students just the most basic tools needed to do some relatively straightforward textual analysis. The book jumps straight into the examples almost from the very first page. The obvious virtue of this approach is that you can start doing some interesting work rather quickly, and as long as your own research doesn't depart dramatically from the examples given in the book you should be able to use the books as a reference and a primer for your own work. However, if you have some slightly more demanding problems that you are trying to work on, then after finishing this book you might want to go to a specialized book on R programming that will give you enough foundation to work on a larger variety of problems.

The book takes the freely available text file of "Moby Dick" and runs a variety of textual analysis on it: simple word count and word frequencies, correlations between various "special" words, context analysis, etc. In the latter chapters it moves from a single book to a corpus of books for more interesting look at themes across many texts. I found the last chapter on topic modeling especially fascinating, but way too brief. I guess I will now have to take a look at other sources to learn more about this line of analysis.

This books is very pedagogical in its style. Oftentimes the author would present two different solutions to a particular problem - one using a very simple yet hard to understand R command, and another broken down into several self-contained chunks. I find this approach very educational and helpful.

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.

**** Electronic version of the book provided for review purposes. ****

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Apr 17, 2015 Joe rated it really liked it

Good overall introduction to R and how to use it to import text data and analyze it. I stopped at page 90 because it got into parsing XML, which is more than I need for the project I'm working on. Readers will probably struggle if they've never done any computer programming, but I did ok with only some programming experience. Good overall introduction to R and how to use it to import text data and analyze it. I stopped at page 90 because it got into parsing XML, which is more than I need for the project I'm working on. Readers will probably struggle if they've never done any computer programming, but I did ok with only some programming experience. ...more

Engaging writing, with code samples and practices. As for programming, I thought the code quality was somewhat low or sloppy, but Jockers is not a software developer by trade. While reading, I did have a few ideas to solve some text-matching issues across systems, and generally, I found the lack of discipline in the author's approach conducive to flexible thinking about using techniques with R. Engaging writing, with code samples and practices. As for programming, I thought the code quality was somewhat low or sloppy, but Jockers is not a software developer by trade. While reading, I did have a few ideas to solve some text-matching issues across systems, and generally, I found the lack of discipline in the author's approach conducive to flexible thinking about using techniques with R. ...more