Download or read book Text Analysis for the Social Sciences written by Carl W. Roberts and published by Routledge. This book was released on 2020-07-24 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides descriptions and illustrations of cutting-edge text analysis methods for communication and marketing research; cultural, historical-comparative, and event analysis; curriculum evaluation; psychological diagnosis; language development research; and for any research in which statistical inferences are drawn from samples of texts. Although the book is accessible to readers having no experience with content analysis, the text analysis expert will find substantial new material in its pages. In particular, this collection describes developments in semantic and network text analysis methodologies that heretofore have been accessible only among a smattering of methodology journals. The book's international and cross-disciplinary content illustrates the breadth of quantitative text analysis applications. These applications demonstrate the methods' utility for international research, as well as for practitioners from the fields of sociology, political science, journalism/communication, computer science, marketing, education, and English. This is an "ecumenical" collection that contains applications not only of the most recent semantic and network text analysis methods, but also of the more traditional thematic method of text analysis. In fact, it is originally with this volume that these two "relational" approaches to text analysis are defined and contrasted with more traditional "thematic" text analysis methods. The emphasis here is on application. The book's chapters provide guidance regarding the sorts of inferences that each method affords, and up-to-date descriptions of the human and technological resources required to apply the methods. Its purpose is as a resource for making quantitative text analysis methods more accessible to social science researchers.
Download or read book Text Mining for Qualitative Data Analysis in the Social Sciences written by Gregor Wiedemann and published by Springer. This book was released on 2016-08-23 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gregor Wiedemann evaluates text mining applications for social science studies with respect to conceptual integration of consciously selected methods, systematic optimization of algorithms and workflows, and methodological reflections relating to empirical research. In an exemplary study, he introduces workflows to analyze a corpus of around 600,000 newspaper articles on the subject of “democratic demarcation” in Germany. He provides a valuable resource for innovative measures to social scientists and computer scientists in the field of applied natural language processing.
Download or read book Text Mining written by Gabe Ignatow and published by SAGE Publications. This book was released on 2016-04-20 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt: Online communities generate massive volumes of natural language data and the social sciences continue to learn how to best make use of this new information and the technology available for analyzing it. Text Mining brings together a broad range of contemporary qualitative and quantitative methods to provide strategic and practical guidance on analyzing large text collections. This accessible book, written by a sociologist and a computer scientist, surveys the fast-changing landscape of data sources, programming languages, software packages, and methods of analysis available today. Suitable for novice and experienced researchers alike, the book will help readers use text mining techniques more efficiently and productively.
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 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 Analysis in Python for Social Scientists written by Dirk Hovy and published by Cambridge University Press. This book was released on 2021-01-21 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: Text is everywhere, and it is a fantastic resource for social scientists. However, because it is so abundant, and because language is so variable, it is often difficult to extract the information we want. There is a whole subfield of AI concerned with text analysis (natural language processing). Many of the basic analysis methods developed are now readily available as Python implementations. This Element will teach you when to use which method, the mathematical background of how it works, and the Python code to implement it.
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 Quantitative Social Science written by Kosuke Imai and published by Princeton University Press. This book was released on 2021-03-16 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Princeton University Press published Imai's textbook, Quantitative Social Science: An Introduction, an introduction to quantitative methods and data science for upper level undergrads and graduates in professional programs, in February 2017. What is distinct about the book is how it leads students through a series of applied examples of statistical methods, drawing on real examples from social science research. The original book was prepared with the statistical software R, which is freely available online and has gained in popularity in recent years. But many existing courses in statistics and data sciences, particularly in some subject areas like sociology and law, use STATA, another general purpose package that has been the market leader since the 1980s. We've had several requests for STATA versions of the text as many programs use it by default. This is a "translation" of the original text, keeping all the current pedagogical text but inserting the necessary code and outputs from STATA in their place"--
Download or read book Analyzing Text and Discourse written by Kristina Boréus and published by SAGE. This book was released on 2017-02-16 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: Clearly setting out the advantages and disadvantages of each methodology, and providing real-world examples of when the methodology has been used successfully, this introduction makes it easy for students to assess which approach would be best for their research and to implement it successfully.
Download or read book Data Analysis for Social Science written by Elena Llaudet and published by Princeton University Press. This book was released on 2022-11-29 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Data analysis has become a necessary skill across the social sciences, and recent advancements in computing power have made knowledge of programming an essential component. Yet most data science books are intimidating and overwhelming to a non-specialist audience, including most undergraduates. This book will be a shorter, more focused and accessible version of Kosuke Imai's Quantitative Social Science book, which was published by Princeton in 2018 and has been adopted widely in graduate level courses of the same title. This book uses the same innovative approach as Quantitative Social Science , using real data and 'R' to answer a wide range of social science questions. It assumes no prior knowledge of statistics or coding. It starts with straightforward, simple data analysis and culminates with multivariate linear regression models, focusing more on the intuition of how the math works rather than the math itself. The book makes extensive use of data visualizations, diagrams, pictures, cartoons, etc., to help students understand and recall complex concepts, provides an easy to follow, step-by-step template of how to conduct data analysis from beginning to end, and will be accompanied by supplemental materials in the appendix and online for both students and instructors"--
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.
Download or read book Data Analytics for the Social Sciences written by G. David Garson and published by Routledge. This book was released on 2021-11-30 with total page 704 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Analytics for the Social Sciences is an introductory, graduate-level treatment of data analytics for social science. It features applications in the R language, arguably the fastest growing and leading statistical tool for researchers. The book starts with an ethics chapter on the uses and potential abuses of data analytics. Chapters 2 and 3 show how to implement a broad range of statistical procedures in R. Chapters 4 and 5 deal with regression and classification trees and with random forests. Chapter 6 deals with machine learning models and the "caret" package, which makes available to the researcher hundreds of models. Chapter 7 deals with neural network analysis, and Chapter 8 deals with network analysis and visualization of network data. A final chapter treats text analysis, including web scraping, comparative word frequency tables, word clouds, word maps, sentiment analysis, topic analysis, and more. All empirical chapters have two "Quick Start" exercises designed to allow quick immersion in chapter topics, followed by "In Depth" coverage. Data are available for all examples and runnable R code is provided in a "Command Summary". An appendix provides an extended tutorial on R and RStudio. Almost 30 online supplements provide information for the complete book, "books within the book" on a variety of topics, such as agent-based modeling. Rather than focusing on equations, derivations, and proofs, this book emphasizes hands-on obtaining of output for various social science models and how to interpret the output. It is suitable for all advanced level undergraduate and graduate students learning statistical data analysis.
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 Qualitative Analysis for Social Scientists written by Anselm L. Strauss and published by Cambridge University Press. This book was released on 1987-06-26 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: The teaching of qualitative analysis in the social sciences is rarely undertaken in a structured way. This handbook is designed to remedy that and to present students and researchers with a systematic method for interpreting qualitative data', whether derived from interviews, field notes, or documentary materials. The special emphasis of the book is on how to develop theory through qualitative analysis. The reader is provided with the tools for doing qualitative analysis, such as codes, memos, memo sequences, theoretical sampling and comparative analysis, and diagrams, all of which are abundantly illustrated by actual examples drawn from the author's own varied qualitative research and research consultations, as well as from his research seminars. Many of the procedural discussions are concluded with rules of thumb that can usefully guide the researchers' analytic operations. The difficulties that beginners encounter when doing qualitative analysis and the kinds of persistent questions they raise are also discussed, as is the problem of how to integrate analyses. In addition, there is a chapter on the teaching of qualitative analysis and the giving of useful advice during research consultations, and there is a discussion of the preparation of material for publication. The book has been written not only for sociologists but for all researchers in the social sciences and in such fields as education, public health, nursing, and administration who employ qualitative methods in their work.
Download or read book Text Analytics written by Domenica Fioredistella Iezzi and published by Springer Nature. This book was released on 2020-11-24 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focusing on methodologies, applications and challenges of textual data analysis and related fields, this book gathers selected and peer-reviewed contributions presented at the 14th International Conference on Statistical Analysis of Textual Data (JADT 2018), held in Rome, Italy, on June 12-15, 2018. Statistical analysis of textual data is a multidisciplinary field of research that has been mainly fostered by statistics, linguistics, mathematics and computer science. The respective sections of the book focus on techniques, methods and models for text analytics, dictionaries and specific languages, multilingual text analysis, and the applications of text analytics. The interdisciplinary contributions cover topics including text mining, text analytics, network text analysis, information extraction, sentiment analysis, web mining, social media analysis, corpus and quantitative linguistics, statistical and computational methods, and textual data in sociology, psychology, politics, law and marketing.
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 Causal Mapping for Research in Information Technology written by V. K. Nakayama and published by IGI Global. This book was released on 2005-01-01 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The causal mapping method has been used in a variety of research areas. The purpose of this book is to provide an introduction to causal mapping for IS researchers and practitioners, providing them everything they need to use causal mapping for both research and application"--Provided by publisher.
