Text Mining and Analysis in the Humanities

This Companion offers a thorough, concise overview of the emerging field of humanities computing. Contains 37 original articles written by leaders in the field. Addresses the central concerns shared by those interested in the subject. Major sections focus on the experience of particular disciplines in applying computational methods to research problems; the basic principles of humanities computing; specific applications and methods; and production, dissemination and archiving. Accompanied by a website featuring supplementary materials, standard readings in the field and essays to be included in future editions of the Companion.

WINNER OF THE NATIONAL BOOK CRITICS CIRCLE AWARD How does a literary historian end up thinking in terms of z-scores, principal component analysis, and clustering coefficients? The essays in "Distant Reading" led to a new and often contested paradigm of literary analysis. In presenting them here Franco Moretti reconstructs his intellectual trajectory, the theoretical influences over his work, and explores the polemics that have often developed around his positions. From the evolutionary model of "Modern European Literature," through the geo-cultural insights of "Conjectures of World Literature" and "Planet Hollywood," to the quantitative findings of "Style, inc." and the abstract patterns of "Network Theory, Plot Analysis," the book follows two decades of conceptual development, organizing them around the metaphor of "distant reading," that has come to define--well beyond the wildest expectations of its author--a growing field of unorthodox literary studies.

Stanford Professor Franco Moretti argues heretically that literature scholars should stop reading books and start counting, graphing and mapping them instead. He insists that such a move could bring new luster to a tired field, one that in some respects, he says, is among the most backwards disciplines in the academy. Literary study, he argues, has been random and unsystematic. For any given periods scholars focus on a select group of a mere few hundred texts: the canon. As a result, they have allowed a narrow distorting slice of history to pass for the total picture. Professor Moretti offers bar charts, maps and time lines instead. His is a history of literature as data points. Charting not only the 18th-century British novel but entire genres - the epistolary, the gothic and the historical novel - as well as the literary output of countries like Japan, Italy, Spain and Nigeria, he shows literary history looks significantly different from what is commonly supposed.

More and more historical texts are becoming available in digital form. Digitization of paper documents is motivated by the aim of preserving cultural heritage and making it more accessible, both to laypeople and scholars. As digital images cannot be searched for text, digitization projects increasingly strive to create digital text, which can be searched and otherwise automatically processed, in addition to facsimiles. Indeed, the emerging field of digital humanities heavily relies on the availability of digital text for its studies.Together with the increasing availability of historical texts in digital form, there is a growing interest in applying natural language processing (NLP) methods and tools to historical texts. However, the specific linguistic properties of historical texts -- the lack of standardized orthography, in particular -- pose special challenges for NLP.This book aims to give an introduction to NLP for historical texts and an overview of the state of the art in this field. The book starts with an overview of methods for the acquisition of historical texts (scanning and OCR), discusses text encoding and annotation schemes, and presents examples of corpora of historical texts in a variety of languages. The book then discusses specific methods, such as creating part-of-speech taggers for historical languages or handling spelling variation. A final chapter analyzes the relationship between NLP and the digital humanities.Certain recently emerging textual genres, such as SMS, social media, and chat messages, or newsgroup and forum postings share a number of properties with historical texts, for example, nonstandard orthography and grammar, and profuse use of abbreviations. The methods and techniques required for the effective processing of historical texts are thus also of interest for research in other domains.Table of Contents: Introduction / NLP and Digital Humanities / Spelling in Historical Texts / Acquiring Historical Texts / Text Encoding and Annotation Schemes / Handling Spelling Variation / NLP Tools for Historical Languages / Historical Corpora / Conclusion / Bibliography

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.

This book comprises a set of articles that specify the methodology of text mining, describe the creation of lexical resources in the framework of text mining and use text mining for various tasks in natural language processing (NLP). The analysis of large amounts of textual data is a prerequisite to build lexical resources such as dictionaries and ontologies and also has direct applications in automated text processing in fields such as history, healthcare and mobile applications, just to name a few. This volume gives an update in terms of the recent gains in text mining methods and reflects the most recent achievements with respect to the automatic build-up of large lexical resources. It addresses researchers that already perform text mining, and those who want to enrich their battery of methods. Selected articles can be used to support graduate-level teaching. The book is suitable for all readers that completed undergraduate studies of computational linguistics, quantitative linguistics, computer science and computational humanities. It assumes basic knowledge of computer science and corpus processing as well as of statistics.

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.

The application of new computational techniques and visualisation technologies in the Arts and Humanities are resulting in fresh approaches and methodologies for the study of new and traditional corpora. This 'computational turn' takes the methods and techniques from computer science to create innovative means of close and distant reading. This book discusses the implications and applications of 'Digital Humanities' and the questions raised when using algorithmic techniques. Key researchersin the field provide a comprehensive introduction to important debates surrounding issues such as the contrast between narrative versus database, pattern-matching versus hermeneutics, and the statistical paradigm versus the data mining paradigm. Also discussed are the new forms of collaboration within the Arts and Humanities that are raised through modular research teams and new organisational structures, as well as techniques for collaborating in an interdisciplinary way.