A Manual Of Textual Analysis Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of A Manual Of Textual Analysis book. This book definitely worth reading, it is an incredibly well-written.
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.
A theory and method of textual analysis which according to the author, may be applied to the transmission and embodiment in any form of any idea or complex of ideas, but was developed specifically to deal with the complicated interrelations of the manuscripts of the Bible, especially of the Greek New Testament.
`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.
Introducing Electronic Text Analysis is a practical and much needed introduction to corpora – bodies of linguistic data. Written specifically for students studying this topic for the first time, the book begins with a discussion of the underlying principles of electronic text analysis. It then examines how these corpora enhance our understanding of literary and non-literary works. In the first section the author introduces the concepts of concordance and lexical frequency, concepts which are then applied to a range of areas of language study. Key areas examined are the use of on-line corpora to complement traditional stylistic analysis, and the ways in which methods such as concordance and frequency counts can reveal a particular ideology within a text. Presenting an accessible and thorough understanding of the underlying principles of electronic text analysis, the book contains abundant illustrative examples and a glossary with definitions of main concepts. It will also be supported by a companion website with links to on-line corpora so that students can apply their knowledge to further study. The accompanying website to this book can be found at http://www.routledge.com/textbooks/0415320216
The Coding Manual for Qualitative Researchers is unique in providing, in one volume, an in-depth guide to each of the multiple approaches available for coding qualitative data. In total, 29 different approaches to coding are covered, ranging in complexity from beginner to advanced level and covering the full range of types of qualitative data from interview transcripts to field notes. For each approach profiled, Johnny Saldaña discusses the method’s origins in the professional literature, a description of the method, recommendations for practical applications, and a clearly illustrated example.
Author : Kathleen Callow Publisher : University Press of America Page : 404 pages File Size : 21,73 MB Release : 1998 Category : Language Arts & Disciplines ISBN :
Copublished with the Summer Institute of Linguistics, Man and Message provides a practical method of analyzing texts based on a cognitive, multilevel model of meaning presented in simple, non-technical language for a wide audience.
Qualitative content analysis is a powerful method for analyzing large amounts of qualitative data collected through interviews or focus groups. It is frequently employed by students, but introductory textbooks on content analysis have largely focused on the quantitative version of the method. In one of the first to focus on qualitative content analysis, Margrit Schreier takes students step-by step through: - creating a coding frame - segmenting the material - trying out the coding frame - evaluating the trial coding - carrying out the main coding - what comes after qualitative content analysis - making use of software when conducting qualitative content analysis. Each part of the process is described in detail and research examples are provided to illustrate each step. Frequently asked questions are answered, the most important points are summarized, and end of chapter questions provide an opportunity to revise these points. After reading the book, students are fully equiped to conduct their own qualitative content analysis. Designed for upper level undergraduate, MA, PhD students and researchers across the social sciences, this is essential reading for all those who want to use qualitative content analysis.
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
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.