Textual analysis is a methodology - a way of gathering data - for researchers who are interested in the ways in which people make sense of the world.

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.

How can you analyse narratives, interviews, field notes, or focus group data? Qualitative text analysis is ideal for these types of data and this textbook provides a hands-on introduction to the method and its theoretical underpinnings. It offers step-by-step instructions for implementing the three principal types of qualitative text analysis: thematic, evaluative, and type-building. Special attention is paid to how to present your results and use qualitative data analysis software packages, which are highly recommended for use in combination with qualitative text analysis since they allow for fast, reliable, and more accurate analysis. The book shows in detail how to use software, from transcribing the verbal data to presenting and visualizing the results. The book is intended for Master’s and Doctoral students across the social sciences and for all researchers concerned with the systematic analysis of texts of any kind.

Chapter 7. Case Study : Comparing Twitter Archives; Getting the Data and Distribution of Tweets; Word Frequencies; Comparing Word Usage; Changes in Word Use; Favorites and Retweets; Summary; Chapter 8. Case Study : Mining NASA Metadata; How Data Is Organized at NASA; Wrangling and Tidying the Data; Some Initial Simple Exploration; Word Co-ocurrences and Correlations; Networks of Description and Title Words; Networks of Keywords; Calculating tf-idf for the Description Fields; What Is tf-idf for the Description Field Words?; Connecting Description Fields to Keywords; Topic Modeling.

American monetary policy is formulated by the Federal Reserve and overseen by Congress. Both policy making and oversight are deliberative processes, although the effect of this deliberation has been difficult to quantify. In this book, Cheryl Schonhardt-Bailey provides a systematic examination of deliberation on monetary policy from 1976 to 2008 by the Federal Reserve's Open Market Committee (FOMC) and House and Senate banking committees. Her innovative account employs automated textual analysis software to study the verbatim transcripts of FOMC meetings and congressional hearings; these empirical data are supplemented and supported by in-depth interviews with participants in these deliberations. The automated textual analysis measures the characteristic words, phrases, and arguments of committee members; the interviews offer a way to gauge the extent to which the empirical findings accord with the participants' personal experiences --

In this new book, you’ll learn how to teach evidence-based writing using a variety of tools, activities, and sample literary texts. Showing elementary and middle school students how to think critically about what they’re reading can be a challenge, but author C. Brian Taylor makes it easy by presenting twelve critical thinking tools along with step-by-step instructions for implementing each one effectively in the classroom. You’ll learn how to: Design units and lesson plans that gradually introduce your students to more complex levels of textual analysis; Encourage students to dig deeper by using the 12 Tools for Critical Thinking; Help students identify context and analyze quotes with the Evidence Finder graphic organizer; Use the Secret Recipe strategy to construct persuasive evidence-based responses that analyze a text’s content or technique; Create Cue Cards to teach students how to recognize and define common literary devices. The book also offers a series of extra examples using mentor texts, so you can clearly see how the strategies in this book can be applied to excerpts from popular, canonical, and semi-historical literature. Additionally, a number of the tools and templates in the book are available as free eResources from our website (http://www.routledge.com/9781138950658), so you can start using them immediately in your classroom.

"The book is an essential resource seeking to analyze real texts and discourse."--BOOK JACKET.