Distant Horizons by Ted UnderwoodJust as a traveler crossing a continent won't sense the curvature of the earth, one lifetime of reading can't grasp the largest patterns organizing literary history. This is the guiding premise behind Distant Horizons, which uses the scope of data newly available to us through digital libraries to tackle previously elusive questions about literature. Ted Underwood shows how digital archives and statistical tools, rather than reducing words to numbers (as is often feared), can deepen our understanding of issues that have always been central to humanistic inquiry. Without denying the usefulness of time-honored approaches like close reading, narratology, or genre studies, Underwood argues that we also need to read the larger arcs of literary change that have remained hidden from us by their sheer scale. Using both close and distant reading to trace the differentiation of genres, transformation of gender roles, and surprising persistence of aesthetic judgment, Underwood shows how digital methods can bring into focus the larger landscape of literary history and add to the beauty and complexity we value in literature.
Publication Date: 2019-02-14
Text Analytics with Python by Dipanjan SarkarDerive useful insights from your data using Python. You will learn both basic and advanced concepts, including text and language syntax, structure, and semantics. You will focus on algorithms and techniques, such as text classification, clustering, topic modeling, and text summarization. Text Analytics with Python teaches you the techniques related to natural language processing and text analytics, and you will gain the skills to know which technique is best suited to solve a particular problem. You will look at each technique and algorithm with both a bird's eye view to understand how it can be used as well as with a microscopic view to understand the mathematical concepts and to implement them to solve your own problems. What You Will Learn: Understand the major concepts and techniques of natural language processing (NLP) and text analytics, including syntax and structure Build a text classification system to categorize news articles, analyze app or game reviews using topic modeling and text summarization, and cluster popular movie synopses and analyze the sentiment of movie reviews Implement Python and popular open source libraries in NLP and text analytics, such as the natural language toolkit (nltk), gensim, scikit-learn, spaCy and Pattern Who This Book Is For : IT professionals, analysts, developers, linguistic experts, data scientists, and anyone with a keen interest in linguistics, analytics, and generating insights from textual data
Publication Date: 2016-12-01
Text Analysis with R for Students of Literature by Matthew L. JockersText 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.
Publication Date: 2014-07-03
Macroanalysis by Matthew L. JockersIn 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.
Ben Fry, Mark Schifferli, Olivia Glennon, Paul Cronan, Martha Durrett, Milo Proscia, Erin Thomas, and Sarah Friedman
The Preservation of Favoured Traces is a series of interactive and print pieces that explores how to show the evolution of a text using Darwin’s On the Origin of Species. Through these studies, we’ve developed ways of showing changes to text over time that are both visually compelling and functional.
Zoë Wilkinson Saldaña
From The Programming Historian: "In this lesson you will learn to conduct ‘sentiment analysis’ on texts and to interpret the results. This is a form of exploratory data analysis based on natural language processing. You will learn to install all appropriate software and to build a reusable program that can be applied to your own texts."
Taylor Arnold and Lauren Tilton
From The Programming Historian: "Learn how to use R to analyze high-level patterns in texts, apply stylometric methods over time and across authors, and use summary methods to describe items in a corpus."
From The Programming Historian: "Corpus analysis is a form of text analysis which allows you to make comparisons between textual objects at a large scale (so-called ‘distant reading’)."
William J. Turkel and Adam Crymble
From The Programming Historian: "Counting the frequency of specific words in a list can provide illustrative data. This lesson will teach you Python’s easy way to count such frequencies."
Shawn Graham, Scott Weingart, and Ian Milligan
From The Programming Historian: "In this lesson you will first learn what topic modeling is and why you might want to employ it in your research. You will then learn how to install and work with the MALLET natural language processing toolkit to do so."