000			02923nam a22003495i 4500
001			91269
005			20231026103946.0
010			_a978-3-030-39643-5 _dcompra
090			_a91269
100			_a20231023d2020 k||y0pory50 ba
101	0		_aeng
102			_aCH
200	1		_aText analysis with r _bDocumento eletrónico _efor students of literature _fby Matthew L. Jockers, Rosamond Thalken
205			_a2nd ed.
210			_aCham _cSpringer International Publishing _cSpringer _d2020
215			_aXXIII, 277 p. _cil.
225	2		_aQuantitative Methods in the Humanities and Social Sciences
303			_aNow 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.
606			_aMathematical statistics _xData processing
606			_aComputational linguistics
606			_aSocial sciences _xStatistical methods
606			_aDigital humanities
606			_aLiterature and technology
606			_aMass media and literature
680			_aQA276.4-.45
700	1		_aJockers _bMatthew L.
701	1		_aThalken _bRosamond
801		0	_aPT _gRPC
856	4		_uhttps://doi.org/10.1007/978-3-030-39643-5
942			_2lcc _cF _n0