Download or read book Analysing Practical and Professional Texts written by Rod Watson and published by Routledge. This book was released on 2016-04-08 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: Analysing Practical and Professional Texts focuses on texts as constituents of human usage, showing how written documents and other 'texts' are integral to social organization. It reveals social organization itself to be not only textually-mediated in nature, but also textually-constituted, showing how texts - professional, technical or otherwise - as well as various social-scientific methodologies employ the resources of ordinary language. Theoretically sophisticated and illustrated with empirical examples, this book will be of interest not only to those with interests in ethnomethodology and conversation analysis, but also to social scientists and anthropologists concerned with text analysis, textual sense and the 'linguistic turn' in the methods of their own disciplines.
Download or read book How to Analyse Texts written by Ronald Carter and published by Routledge. This book was released on 2015-08-20 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Text Analysis with R written by Matthew L. Jockers and published by Springer Nature. This book was released on 2020-03-30 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Analyzing Everyday Texts written by Glenn F. Stillar and published by SAGE. This book was released on 1998-03-10 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book About Writing written by Robin Jeffrey and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Qualitative Text Analysis written by Udo Kuckartz and published by SAGE. This book was released on 2014-01-23 with total page 193 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Text Mining with R written by Julia Silge and published by "O'Reilly Media, Inc.". This book was released on 2017-06-12 with total page 193 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Analysing Media Texts Volume 4 written by Gillespie, Marie and published by McGraw-Hill Education (UK). This book was released on 2006-05-01 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides an introduction to analysing media texts. This book with its award winning DVD, helps students learn how to do semiotic, genre and narrative analysis, content and discourse analysis, and engage with debates about the politics of representation.
Download or read book Computer Assisted Text Analysis written by Roel Popping and published by SAGE. This book was released on 2000-02-28 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: A guide to the methods for the computer-based quantitative analysis of texts. The book concentrates on the methodological and practical issues of coding and handling data including sampling, reliability and validity issues. It focuses on three main approaches to text analysis: lexical, semantic and network. The author: provides an overview of the background and concepts in the field; introduces newer developments; and looks at the relationship between content analysis and other kinds of text analysis. The work concludes with an appendix of computer programmes for text analysis.
Download or read book Supervised Machine Learning for Text Analysis in R written by Emil Hvitfeldt and published by CRC Press. This book was released on 2021-10-22 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Practical Text Mining and Statistical Analysis for Non structured Text Data Applications written by Gary Miner and published by Academic Press. This book was released on 2012-01-11 with total page 1096 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The world contains an unimaginably vast amount of digital information which is getting ever vaster ever more rapidly. This makes it possible to do many things that previously could not be done: spot business trends, prevent diseases, combat crime and so on. Managed well, the textual data can be used to unlock new sources of economic value, provide fresh insights into science and hold governments to account. As the Internet expands and our natural capacity to process the unstructured text that it contains diminishes, the value of text mining for information retrieval and search will increase dramatically. This comprehensive professional reference brings together all the information, tools and methods a professional will need to efficiently use text mining applications and statistical analysis. The Handbook of Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications presents a comprehensive how- to reference that shows the user how to conduct text mining and statistically analyze results. In addition to providing an in-depth examination of core text mining and link detection tools, methods and operations, the book examines advanced preprocessing techniques, knowledge representation considerations, and visualization approaches. Finally, the book explores current real-world, mission-critical applications of text mining and link detection using real world example tutorials in such varied fields as corporate, finance, business intelligence, genomics research, and counterterrorism activities"--
Download or read book Methods of Text and Discourse Analysis written by Stefan Titscher and published by SAGE. This book was released on 2000-07-26 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: ′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.
Download or read book Text as Data written by Justin Grimmer and published by Princeton University Press. This book was released on 2022-03-29 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Text Data Management and Analysis written by ChengXiang Zhai and published by Morgan & Claypool. This book was released on 2016-06-30 with total page 634 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. This has led to an increasing demand for powerful software tools to help people analyze and manage vast amounts of text data effectively and efficiently. Unlike data generated by a computer system or sensors, text data are usually generated directly by humans, and are accompanied by semantically rich content. As such, text data are especially valuable for discovering knowledge about human opinions and preferences, in addition to many other kinds of knowledge that we encode in text. In contrast to structured data, which conform to well-defined schemas (thus are relatively easy for computers to handle), text has less explicit structure, requiring computer processing toward understanding of the content encoded in text. The current technology of natural language processing has not yet reached a point to enable a computer to precisely understand natural language text, but a wide range of statistical and heuristic approaches to analysis and management of text data have been developed over the past few decades. They are usually very robust and can be applied to analyze and manage text data in any natural language, and about any topic. This book provides a systematic introduction to all these approaches, with an emphasis on covering the most useful knowledge and skills required to build a variety of practically useful text information systems. The focus is on text mining applications that can help users analyze patterns in text data to extract and reveal useful knowledge. Information retrieval systems, including search engines and recommender systems, are also covered as supporting technology for text mining applications. The book covers the major concepts, techniques, and ideas in text data mining and information retrieval from a practical viewpoint, and includes many hands-on exercises designed with a companion software toolkit (i.e., MeTA) to help readers learn how to apply techniques of text mining and information retrieval to real-world text data and how to experiment with and improve some of the algorithms for interesting application tasks. The book can be used as a textbook for a computer science undergraduate course or a reference book for practitioners working on relevant problems in analyzing and managing text data.
Download or read book Applied Text Analysis with Python written by Benjamin Bengfort and published by "O'Reilly Media, Inc.". This book was released on 2018-06-11 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Advances in Written Text Analysis written by Malcolm Coulthard and published by Routledge. This book was released on 2002-11-01 with total page 422 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work provides an overview of a wide range of approaches to written text analysis. It includes both classic and specially commissioned papers by distinguished authors, which share a common linguistic framework. The pieces contain a variety of focuses from the patterning of paragraphs, sections or whole texts to the organization of clauses, individual expressions and single words, as well as a variety of text-types. The examples used range from pure science through social science, academic journals, weekly magazines and newspapers, to literary narratives. This collection forms the basis for an course on written text analysis that should be of interest to advanced undergraduate and postgraduate students.
Download or read book An Introduction to Text Mining written by Gabe Ignatow and published by SAGE Publications. This book was released on 2017-09-22 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: Students in social science courses communicate, socialize, shop, learn, and work online. When they are asked to collect data for course projects they are often drawn to social media platforms and other online sources of textual data. There are many software packages and programming languages available to help students collect data online, and there are many texts designed to help with different forms of online research, from surveys to ethnographic interviews. But there is no textbook available that teaches students how to construct a viable research project based on online sources of textual data such as newspaper archives, site user comment archives, digitized historical documents, or social media user comment archives. Gabe Ignatow and Rada F. Mihalcea's new text An Introduction to Text Mining will be a starting point for undergraduates and first-year graduate students interested in collecting and analyzing textual data from online sources, and will cover the most critical issues that students must take into consideration at all stages of their research projects, including: ethical and philosophical issues; issues related to research design; web scraping and crawling; strategic data selection; data sampling; use of specific text analysis methods; and report writing.
