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For well over a century, academic disciplines have studied human behavior using quantitative information. Until recently, however, the humanities have remained largely immune to the use of data-or vigorously resisted it. Thanks to new developments in computer science and natural language processing, literary scholars have embraced the quantitative study of literary works and have helped make Digital Humanities a rapidly growing field. But these developments raise a fundamental, and as yet unanswered question: what is the meaning of literary quantity? In Enumerations, Andrew Piper answers that question across a variety of domains fundamental to the study of literature. He focuses on the elementary particles of literature, from the role of punctuation in poetry, the matter of plot in novels, the study of topoi, and the behavior of characters, to the nature of fictional language and the shape of a poet's career. How does quantity affect our understanding of these categories? What happens when we look at 3,388,230 punctuation marks, 1.4 billion words, or 650,000 fictional characters? Does this change how we think about poetry, the novel, fictionality, character, the commonplace, or the writer's career? In the course of answering such questions, Piper introduces readers to the analytical building blocks of computational text analysis and brings them to bear on fundamental concerns of literary scholarship. This book will be essential reading for anyone interested in Digital Humanities and the future of literary study.

Criticism --- Digital humanities. --- Statistical methods. --- Data processing. --- Criticism - Statistical methods --- Criticism - Data processing --- Digital humanities --- Humanities --- Evaluation of literature --- Literary criticism --- Literature --- Rhetoric --- Aesthetics --- Statistical methods --- Data processing --- Technique --- Evaluation --- kvantitative metoder --- databehandling --- tekstanalyse --- digital humaniora --- litteraturvitenskap --- litteratur

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This Element tackles the problem of generalization with respect to text-based evidence in the field of literary studies. When working with texts, how can we move, reliably and credibly, from individual observations to more general beliefs about the world? The onset of computational methods has highlighted major shortcomings of traditional approaches to texts when it comes to working with small samples of evidence. This Element combines a machine learning-based approach to detect the prevalence and nature of generalization across tens of thousands of sentences from different disciplines alongside a robust discussion of potential solutions to the problem of the generalizability of textual evidence. It exemplifies the way mixed methods can be used in complementary fashion to develop nuanced, evidence-based arguments about complex disciplinary issues in a data-driven research environment.

Criticism, Textual. --- Data Mining. --- Data mining. --- Digital humanities. --- Exploration de données (Informatique). --- Humanities --- Sciences humaines numériques. --- digital humanities. --- Research.

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Book history --- anno 1800-1899 --- Europe

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Books and reading. --- Reading --- Livres et lecture --- Lecture --- Technological innovations --- Innovations --- Technological innovations.