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| example of a textual analysis: Textual Analysis Alan McKee, 2003-04-03 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. |
| example of a textual analysis: About Writing Robin Jeffrey, 2016 |
| example of a textual analysis: Text Analysis in Translation Christiane Nord, 2006-01-01 Text Analysis in Translation has become a classic in Translation Studies. Based on a functional approach to translation and endebted to pragmatic text linguistics, it suggests a model for translation-oriented source-text analysis applicable to all text types and genres independent of the language and culture pairs involved. Part 1 of the study presents the theoretical framework on which the model is based, and surveys the various concepts of translation theory and text linguistics. Part 2 describes the role and scope of source-text analysis in the translation process and explains why the model is relevant to translation. Part 3 presents a detailed study of the extratextual and intratextual factors and their interaction in the text, using numerous examples from all areas of professional translation. Part 4 discusses the applications of the model to translator training, placing particular emphasis on the selection of material for translation classes, grading the difficulty of translation tasks, and translation quality assessment. The book concludes with the practical analysis of a number of texts and their translations, taking into account various text types and several languages (German, English, Spanish, French, Italian, Portuguese, and Dutch). |
| example of a textual analysis: Text Analysis with R Matthew L. Jockers, Rosamond Thalken, 2020-03-30 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. |
| example of a textual analysis: Qualitative Text Analysis Udo Kuckartz, 2014-01-23 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. |
| example of a textual analysis: Text Mining with R Julia Silge, David Robinson, 2017-06-12 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. |
| example of a textual analysis: Digital Humanities Pedagogy Brett D. Hirsch, 2012 The essays in this collection offer a timely intervention in digital humanities scholarship, bringing together established and emerging scholars from a variety of humanities disciplines across the world. The first section offers views on the practical realities of teaching digital humanities at undergraduate and graduate levels, presenting case studies and snapshots of the authors' experiences alongside models for future courses and reflections on pedagogical successes and failures. The next section proposes strategies for teaching foundational digital humanities methods across a variety of scholarly disciplines, and the book concludes with wider debates about the place of digital humanities in the academy, from the field's cultural assumptions and social obligations to its political visions. (4e de couverture). |
| example of a textual analysis: Data Science in Education Using R Ryan A. Estrellado, Emily Freer, Joshua M. Rosenberg, Isabella C. Velásquez, 2020-10-26 Data Science in Education Using R is the go-to reference for learning data science in the education field. The book answers questions like: What does a data scientist in education do? How do I get started learning R, the popular open-source statistical programming language? And what does a data analysis project in education look like? If you’re just getting started with R in an education job, this is the book you’ll want with you. This book gets you started with R by teaching the building blocks of programming that you’ll use many times in your career. The book takes a learn by doing approach and offers eight analysis walkthroughs that show you a data analysis from start to finish, complete with code for you to practice with. The book finishes with how to get involved in the data science community and how to integrate data science in your education job. This book will be an essential resource for education professionals and researchers looking to increase their data analysis skills as part of their professional and academic development. |
| example of a textual analysis: What Writing Does and How It Does It Charles Bazerman, Paul Prior, 2003-12-08 In What Writing Does and How It Does It, editors Charles Bazerman and Paul Prior offer a sophisticated introduction to methods for understanding, studying, and analyzing texts and writing practices. This volume addresses a variety of approaches to analyzing texts, and considers the processes of writing, exploring textual practices and their contexts, and examining what texts do and how texts mean rather than what they mean. Included are traditional modes of analysis (rhetorical, literary, linguistic), as well as newer modes, such as text and talk, genre and activity analysis, and intertextual analysis. The chapters have been developed to provide answers to a specified set of questions, with each one offering: *a preview of the chapter's content and purpose; *an introduction to basic concepts, referring to key theoretical and research studies in the area; *details on the types of data and questions for which the analysis is best used; *examples from a wide-ranging group of texts, including educational materials, student writing, published literature, and online and electronic media; *one or more applied analyses, with a clear statement of procedures for analysis and illustrations of a particular sample of data; and *a brief summary, suggestions for additional readings, and a set of activities. The side-by-side comparison of methods allows the reader to see the multi-dimensionality of writing, facilitating selection of the best method for a particular research question. The volume contributors are experts from linguistics, communication studies, rhetoric, literary analysis, document design, sociolinguistics, education, ethnography, and cultural psychology, and each utilizes a specific mode of text analysis. With its broad range of methodological examples, What Writing Does and How It Does It is a unique and invaluable resource for advanced undergraduate and graduate students and for researchers in education, composition, ESL and applied linguistics, communication, L1 and L2 learning, print media, and electronic media. It will also be useful in all social sciences and humanities that place importance on texts and textual practices, such as English, writing, and rhetoric. |
| example of a textual analysis: Methods of Text and Discourse Analysis Stefan Titscher, Michael Meyer, Ruth Wodak, Eva Vetter, 2000-07-26 ′This volume is the most comprehensive overview to date of sociologically orientated approaches to text and discourse analysis and is worth reading even for those who are interested only in purely linguistiv approaches to text and discourse. Its main merit, I think, is that it intorduces approaches which up to now have hardley been admitted into the universe of scientific discourse′ - Discourse Studies Methods of Text and Discourse Analysis provides the most comprehensive overview currently available of linguistic and sociological approaches to text and discourse analysis. Among the 10 linguistic and sociological models surveyed in this book some of the more important are Grounded Theory, Content Analysis, Conversation Analysis and Critical Discourse Analysis. The book presents each approach according to a standardised format, which allows for direct systematic comparisons. The fully annotated lists of sources provide readers with an additional means of evaluation of the competing analytical methods. Interdisciplinary and international in its aims, Methods of Text and Discourse Analysis suggests the benefits both linguists and sociologists will derive from a more intimate knowledge of each others′ methods and procedures. |
| example of a textual analysis: Digging Into Literature Joanna Wolfe, Laura Wilder, 2015-11-17 Digging Into Literature reveals the critical strategies that any college student can use for reading, analyzing, and writing about literary texts. It is based on a groundbreaking study of the successful interpretive and argumentative moves of more than a thousand professional and student essays. Full of practical charts and summaries, with plenty of exercises and activities for trying out the strategies, the book convincingly reveals that while great literature is profoundly and endlessly complex, writing cogent and effective essays about it doesn’t have to be. |
| example of a textual analysis: Textual Analysis Bodil Helder, 2011 |
| example of a textual analysis: Analysing Discourse Norman Fairclough, 2003 The book is an essential resource seeking to analyze real texts and discourse.--BOOK JACKET. |
| example of a textual analysis: Handbook of Methodological Approaches to Community-based Research Leonard Jason, David Glenwick, 2016 The Handbook of Methodological Approaches to Community-Based Research is intended to aid the community-oriented researcher in learning about and applying cutting-edge quantitative, qualitative, and mixed methods approaches. |
| example of a textual analysis: Essentials of Narrative Analysis Ruthellen Josselson, Phillip L. Hammack, 2021 The brief, practical texts in the Essentials of Qualitative Methods series introduce social science and psychology researchers to key approaches to capturing phenomena not easily measured quantitatively, offering exciting, nimble opportunities to gather in-depth qualitative data. In this book, Ruthellen Josselson and Phillip L. Hammack introduce readers to Narrative Analysis, a qualitative method that investigates how people make meaning of their lives and experiences in both social and cultural contexts. This method offers researchers a window into how individuals' stories are shaped by the categories they inhabit, such as gender, race, class, and sexual identity, and it preserves the voice of the individual through a close textual analysis of their storytelling. About the Essentials of Qualitative Methods book series: Even for experienced researchers, selecting and correctly applying the right method can be challenging. In this groundbreaking series, leading experts in qualitative methods provide clear, crisp, and comprehensive descriptions of their approach, including its methodological integrity, and its benefits and limitations. Each book includes numerous examples to enable readers to quickly and thoroughly grasp how to leverage these valuable methods-- |
| example of a textual analysis: Text Analytics with Python Dipanjan Sarkar, 2019-05-21 Leverage Natural Language Processing (NLP) in Python and learn how to set up your own robust environment for performing text analytics. This second edition has gone through a major revamp and introduces several significant changes and new topics based on the recent trends in NLP. You’ll see how to use the latest state-of-the-art frameworks in NLP, coupled with machine learning and deep learning models for supervised sentiment analysis powered by Python to solve actual case studies. Start by reviewing Python for NLP fundamentals on strings and text data and move on to engineering representation methods for text data, including both traditional statistical models and newer deep learning-based embedding models. Improved techniques and new methods around parsing and processing text are discussed as well. Text summarization and topic models have been overhauled so the book showcases how to build, tune, and interpret topic models in the context of an interest dataset on NIPS conference papers. Additionally, the book covers text similarity techniques with a real-world example of movie recommenders, along with sentiment analysis using supervised and unsupervised techniques. There is also a chapter dedicated to semantic analysis where you’ll see how to build your own named entity recognition (NER) system from scratch. While the overall structure of the book remains the same, the entire code base, modules, and chapters has been updated to the latest Python 3.x release. What You'll Learn • Understand NLP and text syntax, semantics and structure• Discover text cleaning and feature engineering• Review text classification and text clustering • Assess text summarization and topic models• Study deep learning for NLP Who This Book Is For IT professionals, data analysts, developers, linguistic experts, data scientists and engineers and basically anyone with a keen interest in linguistics, analytics and generating insights from textual data. |
| example of a textual analysis: Textual Analysis Made Easy C. Brian Taylor, 2016-10-04 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. |
| example of a textual analysis: Analyzing Everyday Texts Glenn F. Stillar, 1998-03-10 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. |
| example of a textual analysis: Film Text Analysis Janina Wildfeuer, John A. Bateman, 2016-10-04 This book examines film as a multimodal text and an audiovisual synthesis, bringing together current work within the fields of narratology, philosophy, multimodal analysis, sound as well as cultural studies in order to cover a wide range of international academic interest. The book provides new insights into current work and turns the discussion towards recent research questions and analyses, representing and constituting in each contribution new work in the discipline of film text analysis. With the help of various example analyses, all showing the methodological applicability of the discussed issues, the collection provides novel ways of considering film as one of the most complex and at the same time broadly comprehensible texts. |
| example of a textual analysis: Textual Analysis Alan McKee, 2003-03-04 `Alan McKee presents a student friendly introduction to the analysis of cultural texts. The book highlights the cultural differences in interpretation with an array of fascinating examples. Textual Analysis is written in an accessible style with several useful case studies. Each chapter also includes exercises for classroom′ - Jane Stokes, London Metropolitan University `McKee is a gifted practitioner of the skills he would teach in this book, as well as a lively and engaging writer and one who has a real commitment to making his ideas available to a larger public′ - Henry Jenkins, Massachusetts Institute of Technology This book provides an indispensable basic introduction to textual analysis. McKee starts from the most basic philosophical foundations that underlie the practice and explains why texts are important and what they tell us about the world they represent. Textual Analysis guides students away from finding the `correct′ interpretation of a text and explains why we can′t simply ask audiences about the interpretations they make of texts. Textual Analysis: - points to the importance of context, genre and modality - uses excellent examples drawn from popular culture - provides students with a solid grounding on many of the important concepts underlying media and cultural studies Written in an accessible and straightforward style Textual Analysis: A Beginners Guide will be essential reading for all students of media, cultural and communication studies. |
| example of a textual analysis: Computer-Assisted Text Analysis Roel Popping, 2000-02-28 Providing an up-to-date picture of the main methods for the quantitative analysis of text, this book begins by overviewing the background and the conceptual foundations of the field. The author then covers the traditional thematic approaches of text analysis, followed by an explanation of newer developments in semantic and network text analysis methodologies. Finally, he examines the relationship between content analysis and other kinds of text analysis - from qualitative research, linguistic analysis and information retrieval. Computer-assisted Text Analysis focuses on the methodological and practical issues of coding and handling data, including sampling, reliability and validity issues, and includes a useful appendix of computer programs for text analysis. |
| example of a textual analysis: The SAGE Encyclopedia of Communication Research Methods Mike Allen, 2017-04-11 Communication research is evolving and changing in a world of online journals, open-access, and new ways of obtaining data and conducting experiments via the Internet. Although there are generic encyclopedias describing basic social science research methodologies in general, until now there has been no comprehensive A-to-Z reference work exploring methods specific to communication and media studies. Our entries, authored by key figures in the field, focus on special considerations when applied specifically to communication research, accompanied by engaging examples from the literature of communication, journalism, and media studies. Entries cover every step of the research process, from the creative development of research topics and questions to literature reviews, selection of best methods (whether quantitative, qualitative, or mixed) for analyzing research results and publishing research findings, whether in traditional media or via new media outlets. In addition to expected entries covering the basics of theories and methods traditionally used in communication research, other entries discuss important trends influencing the future of that research, including contemporary practical issues students will face in communication professions, the influences of globalization on research, use of new recording technologies in fieldwork, and the challenges and opportunities related to studying online multi-media environments. Email, texting, cellphone video, and blogging are shown not only as topics of research but also as means of collecting and analyzing data. Still other entries delve into considerations of accountability, copyright, confidentiality, data ownership and security, privacy, and other aspects of conducting an ethical research program. Features: 652 signed entries are contained in an authoritative work spanning four volumes available in choice of electronic or print formats. Although organized A-to-Z, front matter includes a Reader’s Guide grouping entries thematically to help students interested in a specific aspect of communication research to more easily locate directly related entries. Back matter includes a Chronology of the development of the field of communication research; a Resource Guide to classic books, journals, and associations; a Glossary introducing the terminology of the field; and a detailed Index. Entries conclude with References/Further Readings and Cross-References to related entries to guide students further in their research journeys. The Index, Reader’s Guide themes, and Cross-References combine to provide robust search-and-browse in the e-version. |
| example of a textual analysis: Blueprints for Text Analytics Using Python Jens Albrecht, Sidharth Ramachandran, Christian Winkler, 2020-12-04 Turning text into valuable information is essential for businesses looking to gain a competitive advantage. With recent improvements in natural language processing (NLP), users now have many options for solving complex challenges. But it's not always clear which NLP tools or libraries would work for a business's needs, or which techniques you should use and in what order. This practical book provides data scientists and developers with blueprints for best practice solutions to common tasks in text analytics and natural language processing. Authors Jens Albrecht, Sidharth Ramachandran, and Christian Winkler provide real-world case studies and detailed code examples in Python to help you get started quickly. Extract data from APIs and web pages Prepare textual data for statistical analysis and machine learning Use machine learning for classification, topic modeling, and summarization Explain AI models and classification results Explore and visualize semantic similarities with word embeddings Identify customer sentiment in product reviews Create a knowledge graph based on named entities and their relations |
| example of a textual analysis: Analysing Media Texts Andrew Burn, David Parker, 2003-12-01 Andrew Burn and David Parker outline how multi-modality theory can be used to analyze texts whicj employ multiple semiotic modes and media, in such a way that a balanced consideration is given to the characteristics of each mode, how they integrate, and how they distribute textual functions between them. The medthods are rooted in a view of significance as dependent on social context, and fulfilling the social and communicative interests of both producers of textual production and use contingent upon digital formats will also be a determining content of the analytical method. |
| example of a textual analysis: A Companion to Digital Humanities Susan Schreibman, Ray Siemens, John Unsworth, 2008-03-03 This Companion offers a thorough, concise overview of the emerging field of humanities computing. Contains 37 original articles written by leaders in the field. Addresses the central concerns shared by those interested in the subject. Major sections focus on the experience of particular disciplines in applying computational methods to research problems; the basic principles of humanities computing; specific applications and methods; and production, dissemination and archiving. Accompanied by a website featuring supplementary materials, standard readings in the field and essays to be included in future editions of the Companion. |
| example of a textual analysis: How to Analyse Texts Ronald Carter, Angela Goddard, 2015-08-20 How to Analyse Texts is the essential introductory textbook and toolkit for language analysis. This book shows the reader how to undertake detailed, language-focussed, contextually sensitive analyses of a wide range of texts – spoken, written and multimodal. The book constitutes a flexible resource which can be used in different ways across a range of courses and at different levels. This textbook includes: three parts covering research and study skills, language structure and use, and how texts operate in sociocultural contexts a wide range of international real-life texts, including items from South China Morning Post, art’otel Berlin and Metro Sweden, which cover digital and print media, advertising, recipes and much more objectives and skill review for each section, activities, commentaries, suggestions for independent assignments, and an analysis checklist for students to follow a combined glossary and index and a comprehensive further reading section a companion website at www.routledge.com/cw/goddard with further links and exercises for students. Written by two experienced teachers of English Language, How to Analyse Texts is key reading for all students of English language and linguistics. |
| example of a textual analysis: Supervised Machine Learning for Text Analysis in R Emil Hvitfeldt, Julia Silge, 2021-10-22 Text data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language. Supervised Machine Learning for Text Analysis in R explains how to preprocess text data for modeling, train models, and evaluate model performance using tools from the tidyverse and tidymodels ecosystem. Models like these can be used to make predictions for new observations, to understand what natural language features or characteristics contribute to differences in the output, and more. If you are already familiar with the basics of predictive modeling, use the comprehensive, detailed examples in this book to extend your skills to the domain of natural language processing. This book provides practical guidance and directly applicable knowledge for data scientists and analysts who want to integrate unstructured text data into their modeling pipelines. Learn how to use text data for both regression and classification tasks, and how to apply more straightforward algorithms like regularized regression or support vector machines as well as deep learning approaches. Natural language must be dramatically transformed to be ready for computation, so we explore typical text preprocessing and feature engineering steps like tokenization and word embeddings from the ground up. These steps influence model results in ways we can measure, both in terms of model metrics and other tangible consequences such as how fair or appropriate model results are. |
| example of a textual analysis: Content Analysis Klaus Krippendorff, 2004 The Second Edition of Content Analysis: An Introduction to Its Methodology is a definitive sourcebook of the history and core principles of content analysis as well as an essential resource for present and future studies. The book introduces readers to ways of analyzing meaningful matter such as texts, images, voices - that is, data whose physical manifestations are secondary to the meanings that a particular population of people brings to them. Organized into three parts, the book examines the conceptual and methodological aspects of content analysis and also traces several paths through content analysis protocols. The author has completely revised and updated the Second Edition, integrating new information on computer-aided text analysis. The book also includes a practical guide that incorporates experiences in teaching and how to advise academic and commercial researchers. In addition, Krippendorff clarifies the epistemology and logic of content analysis as well as the methods for achieving its aims. Intended as a textbook for advanced undergraduate and graduate students across the social sciences, Content Analysis, Second Edition will also be a valuable resource for practitioners in a variety of disciplines. |
| example of a textual analysis: Multilingual Text Analysis Marina Litvak, Natalia Vanetik, 2019 Text analytics (TA) covers a very wide research area. Its overarching goal is to discover and present knowledge -- facts, rules, and relationships -- that is otherwise hidden in the textual content. The authors of this book guide us in a quest to attain this knowledge automatically, by applying various machine learning techniques. This book describes recent development in multilingual text analysis. It covers several specific examples of practical TA applications, including their problem statements, theoretical background, and implementation of the proposed solution. The reader can see which preprocessing techniques and text representation models were used, how the evaluation process was designed and implemented, and how these approaches can be adapted to multilingual domains. |
| example of a textual analysis: Text Mining and Analysis Dr. Goutam Chakraborty, Murali Pagolu, Satish Garla, 2014-11-22 Big data: It's unstructured, it's coming at you fast, and there's lots of it. In fact, the majority of big data is text-oriented, thanks to the proliferation of online sources such as blogs, emails, and social media. However, having big data means little if you can't leverage it with analytics. Now you can explore the large volumes of unstructured text data that your organization has collected with Text Mining and Analysis: Practical Methods, Examples, and Case Studies Using SAS. This hands-on guide to text analytics using SAS provides detailed, step-by-step instructions and explanations on how to mine your text data for valuable insight. Through its comprehensive approach, you'll learn not just how to analyze your data, but how to collect, cleanse, organize, categorize, explore, and interpret it as well. Text Mining and Analysis also features an extensive set of case studies, so you can see examples of how the applications work with real-world data from a variety of industries. Text analytics enables you to gain insights about your customers' behaviors and sentiments. Leverage your organization's text data, and use those insights for making better business decisions with Text Mining and Analysis. This book is part of the SAS Press program. |
| example of a textual analysis: An Introduction to Statistical Learning Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor, 2023-08-01 An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users. |
| example of a textual analysis: Close Reading: The Basics David Greenham, 2018-07-27 Close reading is the most essential skill that literature students continue to develop across the full length of their studies. This book is the ideal guide to the practice, providing a methodology that can be used for poetry, novels, drama, and beyond. Using classic works of literature, such as Hamlet and The Great Gatsby as case studies, David Greenham presents a unique, contextual approach to close reading, while addressing key questions such as: What is close reading? What is the importance of the relationships between words? How can close reading enhance reading pleasure? Is there a method of close reading that works for all literary genres? How can close reading unlock complexity? How does the practice of close reading relate to other theoretical and critical approaches? Close Reading: The Basics is formulated to bring together reading pleasure and analytic techniques that will engage the student of literature and enhance their reading experience. |
| example of a textual analysis: Script Analysis for Actors, Directors, and Designers James Michael Thomas, 2009 Script Analysis specifically for Actors, Directors, and Designers; the only book on this subject that covers the growing area of unconventional plays. |
| example of a textual analysis: The Media and Communications in Australia Stuart Cunningham, Graeme Turner, 2006 This second edition offers a systematic introduction to this dynamic and often bewidering field. Fully updated and revised to take acount of the latest developments, it outlines the key media industries and explains how communications technologies are impacting on them. |
| example of a textual analysis: Manual of Textual Analysis Vinton A. Dearing, 2023-11-10 This title is part of UC Press's Voices Revived program, which commemorates University of California Press’s mission to seek out and cultivate the brightest minds and give them voice, reach, and impact. Drawing on a backlist dating to 1893, Voices Revived makes high-quality, peer-reviewed scholarship accessible once again using print-on-demand technology. This title was originally published in 1959. |
| example of a textual analysis: Applied Text Analysis with Python Benjamin Bengfort, Rebecca Bilbro, Tony Ojeda, 2018-06-11 From news and speeches to informal chatter on social media, natural language is one of the richest and most underutilized sources of data. Not only does it come in a constant stream, always changing and adapting in context; it also contains information that is not conveyed by traditional data sources. The key to unlocking natural language is through the creative application of text analytics. This practical book presents a data scientist’s approach to building language-aware products with applied machine learning. You’ll learn robust, repeatable, and scalable techniques for text analysis with Python, including contextual and linguistic feature engineering, vectorization, classification, topic modeling, entity resolution, graph analysis, and visual steering. By the end of the book, you’ll be equipped with practical methods to solve any number of complex real-world problems. Preprocess and vectorize text into high-dimensional feature representations Perform document classification and topic modeling Steer the model selection process with visual diagnostics Extract key phrases, named entities, and graph structures to reason about data in text Build a dialog framework to enable chatbots and language-driven interaction Use Spark to scale processing power and neural networks to scale model complexity |
| example of a textual analysis: A Short Guide to Writing about Literature Sylvan Barnet, William Cain, 2013-10-03 A Short Guide to Writing about Literature emphasises writing as a process and incorporates new critical approaches to writing about literature. This edition continues to offer students sound advice on how to become critical thinkers and enrich their reading response through accessible, step-by-step instruction. This highly respected text is ideal as a supplement to any course where writing about literature or literary studies is emphasised. The full text downloaded to your computer With eBooks you can: search for key concepts, words and phrases make highlights and notes as you study share your notes with friends eBooks are downloaded to your computer and accessible either offline through the Bookshelf (available as a free download), available online and also via the iPad and Android apps. Upon purchase, you'll gain instant access to this eBook. Time limit The eBooks products do not have an expiry date. You will continue to access your digital ebook products whilst you have your Bookshelf installed. |
| example of a textual analysis: Compendium for Early Career Researchers in Mathematics Education Gabriele Kaiser, Norma Presmeg, 2019-04-26 The purpose of this Open Access compendium, written by experienced researchers in mathematics education, is to serve as a resource for early career researchers in furthering their knowledge of the state of the field and disseminating their research through publishing. To accomplish this, the book is split into four sections: Empirical Methods, Important Mathematics Education Themes, Academic Writing and Academic Publishing, and a section Looking Ahead. The chapters are based on workshops that were presented in the Early Career Researcher Day at the 13th International Congress on Mathematical Education (ICME-13). The combination of presentations on methodological approaches and theoretical perspectives shaping the field in mathematics education research, as well as the strong emphasis on academic writing and publishing, offered strong insight into the theoretical and empirical bases of research in mathematics education for early career researchers in this field. Based on these presentations, the book provides a state-of-the-art overview of important theories from mathematics education and the broad variety of empirical approaches currently widely used in mathematics education research. This compendium supports early career researchers in selecting adequate theoretical approaches and adopting the most appropriate methodological approaches for their own research. Furthermore, it helps early career researchers in mathematics education to avoid common pitfalls and problems while writing up their research and it provides them with an overview of the most important journals for research in mathematics education, helping them to select the right venue for publishing and disseminating their work. |
| example of a textual analysis: Natural Language Processing with Python Steven Bird, Ewan Klein, Edward Loper, 2009-06-12 This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication. Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify named entities Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful. |
| example of a textual analysis: "They Say Gerald Graff, Cathy Birkenstein, 2016 THIS TITLE HAS BEEN UPDATED TO REFLECT THE 2016 MLA UPDATE. The New York Times best-selling book on academic writing--in use at more than 1,500 schools. |
EXAMPLE Definition & Meaning - Merriam-Webster
The meaning of EXAMPLE is one that serves as a pattern to be imitated or not to be imitated. How to use example in a sentence. Synonym Discussion …
EXAMPLE | English meaning - Cambridge Dictionary
EXAMPLE definition: 1. something that is typical of the group of things that it is a member of: 2. a way of helping…. …
EXAMPLE Definition & Meaning | Dictionary.com
one of a number of things, or a part of something, taken to show the character of the whole. This painting is an example of his early work. a pattern …
Example - definition of example by The Free Dictiona…
1. one of a number of things, or a part of something, taken to show the character of the whole. 2. a pattern or model, as of something to be …
Example Definition & Meaning - YourDictionary
To be illustrated or exemplified (by). Wear something simple; for example, a skirt and blouse.
EXAMPLE Definition & Meaning - Merriam-Webster
The meaning of EXAMPLE is one that serves as a pattern to be imitated or not to be imitated. How to use example in a sentence. Synonym Discussion of Example.
EXAMPLE | English meaning - Cambridge Dictionary
EXAMPLE definition: 1. something that is typical of the group of things that it is a member of: 2. a way of helping…. Learn more.
EXAMPLE Definition & Meaning | Dictionary.com
one of a number of things, or a part of something, taken to show the character of the whole. This painting is an example of his early work. a pattern or model, as of something to be imitated or …
Example - definition of example by The Free Dictionary
1. one of a number of things, or a part of something, taken to show the character of the whole. 2. a pattern or model, as of something to be imitated or avoided: to set a good example. 3. an …
Example Definition & Meaning - YourDictionary
To be illustrated or exemplified (by). Wear something simple; for example, a skirt and blouse.
EXAMPLE - Meaning & Translations | Collins English Dictionary
An example of something is a particular situation, object, or person which shows that what is being claimed is true. 2. An example of a particular class of objects or styles is something that …
example noun - Definition, pictures, pronunciation and usage …
used to emphasize something that explains or supports what you are saying; used to give an example of what you are saying. There is a similar word in many languages, for example in …
Example - Definition, Meaning & Synonyms - Vocabulary.com
An example is a particular instance of something that is representative of a group, or an illustration of something that's been generally described. Example comes from the Latin word …
example - definition and meaning - Wordnik
noun Something that serves as a pattern of behaviour to be imitated (a good example) or not to be imitated (a bad example). noun A person punished as a warning to others. noun A parallel …
EXAMPLE Synonyms: 20 Similar Words - Merriam-Webster
Some common synonyms of example are case, illustration, instance, sample, and specimen. While all these words mean "something that exhibits distinguishing characteristics in its …
