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 k...

Buy Now From Amazon

Product Review

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.

Similar Products

Humanities Data in R: Exploring Networks, Geospatial Data, Images, and Text (Quantitative Methods in the Humanities and Social Sciences)Practical Text Analytics: Interpreting Text and Unstructured Data for Business Intelligence (Marketing Science)Text Mining with R: A Tidy ApproachA User's Guide to Network Analysis in RMacroanalysis: Digital Methods and Literary History (Topics in the Digital Humanities)Statistical Analysis of Network Data with R (Use R!)Graphs, Maps, Trees: Abstract Models for Literary HistoryMastering Text Mining with RUnderstanding Social Networks: Theories, Concepts, and FindingsGood Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations