This book provides a comprehensive overview of approaches to problems of language learning, aimed at graduate students and researchers interested in English for academic purposes. Its primary focus is the complexity and nuances of academic writing such as phraseology, nominalization, thematicity, and phrasal complexity features. The book will clarify the issue of how language is used to communicate discipline-related content, viewed through the lens of linguistics as one of the human sciences. Each chapter concludes with several tasks that enable users to substantiate what has been presented in that chapter. The book primarily addresses non-native speakers of English who are studying for master’s and PhD qualifications through the medium of English; however, non-native researchers may also find some chapters of the book useful for their underlying focus on academic writing and publishing.

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.

A guide for using computational text analysis to learn about the social world From social media posts and text messages to digital government documents and archives, researchers are bombarded with a deluge of text reflecting the social world. This textual data gives unprecedented insights into fundamental questions in the social sciences, humanities, and industry. Meanwhile new machine learning tools are rapidly transforming the way science and business are conducted. Text as Data shows how to combine new sources of data, machine learning tools, and social science research design to develop and evaluate new insights. Text as Data is organized around the core tasks in research projects using text—representation, discovery, measurement, prediction, and causal inference. The authors offer a sequential, iterative, and inductive approach to research design. Each research task is presented complete with real-world applications, example methods, and a distinct style of task-focused research. Bridging many divides—computer science and social science, the qualitative and the quantitative, and industry and academia—Text as Data is an ideal resource for anyone wanting to analyze large collections of text in an era when data is abundant and computation is cheap, but the enduring challenges of social science remain. Overview of how to use text as data Research design for a world of data deluge Examples from across the social sciences and industry

In Analyzing Everyday Texts, author Glenn F. Stillar provides a comprehensive and well-illustrated framework for the analysis of everyday texts by outlining and integrating three different perspectives: discoursal, rhetorical, and social. First, the tools of each perspective are carefully explicated in chapters on the resources of discoursal, rhetorical, and social theory. These three perspectives are then brought together in extensive analyses of various everyday texts. Finally, the book reflects on the principles and consequences of conducting theoretically informed critical textual analysis. For researchers analyzing everyday texts and for scholars teaching theories and methods of analysis, Analyzing Everyday Texts will be an invaluable addition to the current literature.

Recent years have seen an explosion of interest in the use of computerized text analysis methods to address basic psychological questions. This comprehensive handbook brings together leading language analysis scholars to present foundational concepts and methods for investigating human thought, feeling, and behavior using language. Contributors work toward integrating psychological science and theory with natural language processing (NLP) and machine learning. Ethical issues in working with natural language data sets are discussed in depth. The volume showcases NLP-driven techniques and applications in areas including interpersonal relationships, personality, morality, deception, social biases, political psychology, psychopathology, and public health.

In a thoroughgoing revision of the first edition of this classic text and reference, published by Plenum in 1987, the editors have assembled a distinguished group of contributors to address such topics as past, present, and future perspectives on family diversity; theory and methods of the family; changing family patterns and roles; the family and other institutions; and family dynamics and processes.

Analysing Power in Language introduces students to a range of analytical techniques for the critical study of texts.Each section of the book provides an in-depth presentation of a different method of analysis with worked examples and texts for students to analyse and discuss. Answer keys are also provided for the analyses. Taking text analysis as the first step in discourse analysis, Analysing Power in Language: Explores the relationship between the goals of discourse, the social positions of the speakers, the contexts in which they are produced, the audience for which they are intended and the language features chosen Presents a powerful approach to text analysis that reveals the links between language usage and a community’s assumptions, convictions, and understandings Identifies a range of power types, appropriate to different contexts Explains and illustrates a social approach to text analysis with important linguistic concepts woven in seamlessly with examples of discourse Offers concrete guidance in text and discourse analysis with carefully crafted examples and fully illustrated explanations. Incisive and thought-provoking yet also accessible, Analysing Power in Language will be essential reading for advanced undergraduate, postgraduate and research students studying discourse analysis.

Designed to be the essential one-volume resource for students and researchers on Systemic Functional Linguistics.