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Text Analysis with R - For Students of Literature (Paperback, 2nd ed. 2020)
Loot Price: R1,472
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Text Analysis with R - For Students of Literature (Paperback, 2nd ed. 2020)
Series: Quantitative Methods in the Humanities and Social Sciences
Expected to ship within 10 - 15 working days
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Now in its second edition, Text Analysis with R provides a
practical introduction to computational text analysis using the
open source programming language R. R is an extremely popular
programming language, used throughout the sciences; due to its
accessibility, R is now used increasingly in other research areas.
In this volume, readers immediately begin working with text, and
each chapter examines a new technique or process, allowing readers
to obtain a broad exposure to core R procedures and a fundamental
understanding of the possibilities of computational text analysis
at both the micro and the macro scale. Each chapter builds on its
predecessor as readers move from small scale "microanalysis" of
single texts to large scale "macroanalysis" of text corpora, and
each 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. Text Analysis with R 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 toolkit to include quantitative and computational
approaches to the study of text. Computation provides access to
information in text that readers simply cannot gather using
traditional qualitative methods of close reading and human
synthesis. This new edition features two new chapters: one that
introduces dplyr and tidyr in the context of parsing and analyzing
dramatic texts to extract speaker and receiver data, and one on
sentiment analysis using the syuzhet package. It is also filled
with updated material in every chapter to integrate new
developments in the field, current practices in R style, and the
use of more efficient algorithms.
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