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Introduction to Text Visualization by Nan Cao,Weiwei Cui Pdf
This book provides a systematic review of many advanced techniques to support the analysis of large collections of documents, ranging from the elementary to the profound, covering all the aspects of the visualization of text documents. Particularly, we start by introducing the fundamental concept of information visualization and visual analysis, followed by a brief survey of the field of text visualization and commonly used data models for converting document into a structured form for visualization. Then we introduce the key visualization techniques including visualizing document similarity, content, sentiments, as well as text corpus exploration system in details with concrete examples in the rest of the book.
An Introduction to Text Mining by Gabe Ignatow,Rada Mihalcea Pdf
This is the ideal introduction for students seeking to collect and analyze textual data from online sources. It covers the most critical issues that they must take into consideration at all stages of their research projects.
Text Analytics: An Introduction to the Science and Applications of Unstructured Information Analysis is a concise and accessible introduction to the science and applications of text analytics (or text mining), which enables automatic knowledge discovery from unstructured information sources, for both industrial and academic purposes. The book introduces the main concepts, models, and computational techniques that enable the reader to solve real decision-making problems arising from textual and/or documentary sources. Features: Easy-to-follow step-by-step concepts and methods Every chapter is introduced in a very gentle and intuitive way so students can understand the WHYs, WHAT-IFs, WHAT-IS-THIS-FORs, HOWs, etc. by themselves Practical programming exercises in Python for each chapter Includes theory and practice for every chapter, summaries, practical coding exercises for target problems, QA, and sample code and data available for download at https://www.routledge.com/Atkinson-Abutridy/p/book/9781032249797
An accessible primer on how to create effective graphics from data This book provides students and researchers a hands-on introduction to the principles and practice of data visualization. It explains what makes some graphs succeed while others fail, how to make high-quality figures from data using powerful and reproducible methods, and how to think about data visualization in an honest and effective way. Data Visualization builds the reader’s expertise in ggplot2, a versatile visualization library for the R programming language. Through a series of worked examples, this accessible primer then demonstrates how to create plots piece by piece, beginning with summaries of single variables and moving on to more complex graphics. Topics include plotting continuous and categorical variables; layering information on graphics; producing effective “small multiple” plots; grouping, summarizing, and transforming data for plotting; creating maps; working with the output of statistical models; and refining plots to make them more comprehensible. Effective graphics are essential to communicating ideas and a great way to better understand data. This book provides the practical skills students and practitioners need to visualize quantitative data and get the most out of their research findings. Provides hands-on instruction using R and ggplot2 Shows how the “tidyverse” of data analysis tools makes working with R easier and more consistent Includes a library of data sets, code, and functions
Text Data Management and Analysis by ChengXiang Zhai,Sean Massung Pdf
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.
Fundamentals of Data Visualization by Claus O. Wilke Pdf
Effective visualization is the best way to communicate information from the increasingly large and complex datasets in the natural and social sciences. But with the increasing power of visualization software today, scientists, engineers, and business analysts often have to navigate a bewildering array of visualization choices and options. This practical book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures. What visualization type is best for the story you want to tell? How do you make informative figures that are visually pleasing? Author Claus O. Wilke teaches you the elements most critical to successful data visualization. Explore the basic concepts of color as a tool to highlight, distinguish, or represent a value Understand the importance of redundant coding to ensure you provide key information in multiple ways Use the book’s visualizations directory, a graphical guide to commonly used types of data visualizations Get extensive examples of good and bad figures Learn how to use figures in a document or report and how employ them effectively to tell a compelling story
Introduction to Information Visualization by Gerald Benoit Pdf
Introduction to Information Visualization: Transforming Data into Meaningful Information is for anyone interested in the art and science of communicating data to others. It shows readers how to transform data into something meaningful - information. Applying information visualization in research, service, teaching, and professional life requires a solid understanding of graphic design and the aesthetic along with hands-on skills and knowledge of data principles and software. This book is applicable to students in all domains, to researchers who need to understand how to create graphics that explain their data, and to professionals and administrators for professional development training. Website Designers and Human-Computer Interaction researchers will appreciate the backstory of designing interactive visualizations for the web. Drawing on the author’s years of practice and teaching, it bridges the two worlds in ways everyone can participate in the future of information and to appreciate the beautiful in information: Step-by-step directions in the fundamentals of HTML5, CSS, and d3.js Design challenges with fully explained answers Web-site support for code samples (JavaScript, d3.js, python), live examples, and a place to build a community of other IV pros Useful for teaching design to scientists; data to the humanities Guidance for using the text depending on the class makeup Review of third-party visualization software, big data trends, and script libraries Guidance on how to continue in the IV world after graduation This full-color book features graphics and a companion Web site. The online companion site hosts living examples, updates, and errata. You’re invited to participate on the site, too, sharing your questions, solutions, and ideas. For most readings, there is a partner design lab. At the conclusion of the course, there is a complete interactive information visualization service documentation for libraries.
Text Mining with R by Julia Silge,David Robinson Pdf
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.
Introduction to Data Science by Rafael A. Irizarry Pdf
Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.
Introduction to Scientific Visualization by Helen Wright Pdf
This is a ‘how to’ book for scientific visualization. The book does not treat the subject as a subset of information visualisation, but rather as a subject in its own right. An introduction on the philosophy of the subject sets the scene and the theory of colour perception is introduced. Next, using Brodlie’s taxonomy to underpin its core chapters, it is shown how to classify data. Worked examples are given throughout the text and there are practical ‘sidebars’ for readers with access to the IRIS Explorer software who can try out the demonstrations on an accompanying website. The book concludes with a ‘taster’ of ongoing research.
Information visualization is the act of gaining insight into data, and is carried out by virtually everyone. It is usually facilitated by turning data – often a collection of numbers – into images that allow much easier comprehension. Everyone benefits from information visualization, whether internet shopping, investigating fraud or indulging an interest in art. So no assumptions are made about specialist background knowledge in, for example, computer science, mathematics, programming or human cognition. Indeed, the book is directed at two main audiences. One comprises first year students of any discipline. The other comprises graduates – again of any discipline – who are taking a one- or two-year course of training to be visual and interaction designers. By focusing on the activity of design the pedagogical approach adopted by the book is based on the view that the best way to learn about the subject is to do it, to be creative: not to prepare for the ubiquitous examination paper. The content of the book, and the associated exercises, are typically used to support five creative design exercises, the final one being a group project mirroring the activity of a consultancy undertaking a design (not an implementation) for a client. Engagement with the material of this book can have a variety of outcomes. The composer of a school newsletter and the applicant for a multi-million investment should both be able to convey their message more effectively, and the curator of an exhibition will have new presentational techniques on their palette. For those students training to be visual/interaction designers the exercises have led to original and stimulating outcomes.
Text Mining and Analysis by Dr. Goutam Chakraborty,Murali Pagolu,Satish Garla Pdf
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.
What Writing Does and How It Does It by Charles Bazerman,Paul Prior Pdf
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.
The visualization process doesn’t happen in a vacuum; it is grounded in principles and methodologies of design, cognition, perception, and human-computer-interaction that are combined to one’s personal knowledge and creative experiences. Design for Information critically examines other design solutions —current and historic— helping you gain a larger understanding of how to solve specific problems. This book is designed to help you foster the development of a repertoire of existing methods and concepts to help you overcome design problems. Learn the ins and outs of data visualization with this informative book that provides you with a series of current visualization case studies. The visualizations discussed are analyzed for their design principles and methods, giving you valuable critical and analytical tools to further develop your design process. The case study format of this book is perfect for discussing the histories, theories and best practices in the field through real-world, effective visualizations. The selection represents a fraction of effective visualizations that we encounter in this burgeoning field, allowing you the opportunity to extend your study to other solutions in your specific field(s) of practice. This book is also helpful to students in other disciplines who are involved with visualizing information, such as those in the digital humanities and most of the sciences.