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Much of the data available today is unstructured and text-heavy, making it challenging for analysts to apply their usual data wrangling and visualization tools. With this practical book, you’ll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You’ll learn how tidytext and other tidy tools in R can make text analysis easier and more effective.The authors demonstrate how treating text as data frames enables you to manipulate, summarize, and visualize characteristics of text. You’ll also learn how to integrate natural language processing (NLP) into effective workflows. Practical code examples and data explorations will help you generate real insights from literature, news, and social media.Learn how to apply the tidy text format to NLPUse sentiment analysis to mine the emotional content of textIdentify a document’s most important terms with frequency measurementsExplore relationships and connections between words with the ggraph and widyr packagesConvert back and forth between R’s tidy and non-tidy text formatsUse topic modeling to classify document collections into natural groupsExamine case studies that compare Twitter archives, dig into NASA metadata, and analyze thousands of Usenet messages

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.

Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications brings together all the information, tools and methods a professional will need to efficiently use text mining applications and statistical analysis. Winner of a 2012 PROSE Award in Computing and Information Sciences from the Association of American Publishers, this book 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. 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.Extensive case studies, most in a tutorial format, allow the reader to 'click through' the example using a software program, thus learning to conduct text mining analyses in the most rapid manner of learning possibleNumerous examples, tutorials, power points and datasets available via companion website on Elsevierdirect.comGlossary of text mining terms provided in the appendix

Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications brings together all the information, tools and methods a professional will need to efficiently use text mining applications and statistical analysis. Winner of a 2012 PROSE Award in Computing and Information Sciences from the Association of American Publishers, this book 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. 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.Extensive case studies, most in a tutorial format, allow the reader to 'click through' the example using a software program, thus learning to conduct text mining analyses in the most rapid manner of learning possibleNumerous examples, tutorials, power points and datasets available via companion website on Elsevierdirect.comGlossary of text mining terms provided in the appendix

The Dangerous Art of Text Mining: A Methodology for Digital History 1.45

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A hands on guide to web scraping and text mining for both beginners and experienced users of R Introduces fundamental concepts of the main architecture of the web and databases and covers HTTP, HTML, XML, JSON, SQL. Provides basic techniques to query web documents and data sets (XPath and regular expressions). An extensive set of exercises are presented to guide the reader through each technique. Explores both supervised and unsupervised techniques as well as advanced techniques such as data scraping and text management. Case studies are featured throughout along with examples for each technique presented. R code and solutions to exercises featured in the book are provided on a supporting website.

A hands on guide to web scraping and text mining for both beginners and experienced users of R Introduces fundamental concepts of the main architecture of the web and databases and covers HTTP, HTML, XML, JSON, SQL. Provides basic techniques to query web documents and data sets (XPath and regular expressions). An extensive set of exercises are presented to guide the reader through each technique. Explores both supervised and unsupervised techniques as well as advanced techniques such as data scraping and text management. Case studies are featured throughout along with examples for each technique presented. R code and solutions to exercises featured in the book are provided on a supporting website.

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Excerpt from A Text-Book of Ore and Stone MiningThe object of my text-book is to assist students in acquiring a knowledge of the art of mining. Books and lectures are not intended to take the place of practical teaching at mines, but they render the training more thorough and complete in many ways: they serve to explain the principles of the art, to solve difficulties which perplex the beginner, to suggest matters which he should observe, to tell him of things beyond his ken, and to supply him with a system for arranging his ideas methodically.It will be seen by my numerous references that I have not hesitated to avail myself of very varied sources of informa tion, and that I have taken care to avoid dwelling too much upon English examples.About the PublisherForgotten Books publishes hundreds of thousands of rare and classic books. Find more at www.forgottenbooks.comThis book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.

Your power tools for making the complex comprehensible Now more than ever, our students are being asked to do highly advanced thinking, talking, and writing around their reading. If only there were ingenious new tools that could give our students the space to tease apart complex ideas in order to comprehend and weld their understandings into a new whole. Good news: these tools exist―Mining Complex Text. You’ll learn how graphic organizers can: Help students read, reread, and take notes on a text Promote students’ oral sharing of information and their ideas Elevate organized note-making from complex text(s) Scaffold students’ narrative and informational writing

Liu has written a comprehensive text on Web mining, which consists of two parts. The first part covers the data mining and machine learning foundations, where all the essential concepts and algorithms of data mining and machine learning are presented. The second part covers the key topics of Web mining, where Web crawling, search, social network analysis, structured data extraction, information integration, opinion mining and sentiment analysis, Web usage mining, query log mining, computational advertising, and recommender systems are all treated both in breadth and in depth. His book thus brings all the related concepts and algorithms together to form an authoritative and coherent text. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in Web mining and data mining both as a learning text and as a reference book. Professors can readily use it for classes on data mining, Web mining, and text mining. Additional teaching materials such as lecture slides, datasets, and implemented algorithms are available online.

Economic skill is an essential partner to technical skill in every step of the mining process. An economic “mindset” begins before the first drill hole. This new book will help you effectively direct mining operations through the use of innovative economic strategies.The text covers what is meant by a cost-effective mining scheme, the economics of information, and the procedures for rational evaluation of uncertain projects. It defines “ore” from an economic perspective and covers the influence of scheduling on ore reserves.Discounted cash flow techniques, the most widely used evaluation technique for investment decision making, is covered in detail. The assumption of the use of spreadsheets is unique to this book. The application of DCF techniques in an operating mine environment is given expanded coverage and examples are drawn from real-life studies.The differences between economic decision-making—a forward-looking task—and the reporting of results via accounting methods—a backward-looking activity—are reviewed. Capital and decision-making procedures associated with capital investments in a risk environment are given extensive coverage. Case studies for capital investment in an operating mine are included. Comprehensive examples investigate “value” from a risk-reduction perspective and from an “expected return on investment” perspective.This book offers solutions to the problem that many mining projects fail to achieve expectations because of their inability to adapt to change. A new technique is explained that allows calculation of capital that is “at risk” from capital that is not at risk. This promises significant advances in the way that investments are made and capital is valued in the industry.

This text is a comprehensive account of the development of extractive metallurgy from its inception to the Industrial Revolution. Drawing on information gained through state-of-the-art archaeological sampling methods, the secrets of early mining and smelting techniques are revealed, as well as the dramatic technological developments that transformed those processes in the 16th-19th centuries. Integrating documentary evidence with metallurgical study and new information from archaeological excavations in Europe, India, Iran, North America and China, this work offers a full history of mining and metal production worldwide.

Introduction to Business Data Mining was developed to introduce students, as opposed to professional practitioners or engineering students, to the fundamental concepts of data mining. Most importantly, this text shows readers how to gather and analyze large sets of data to gain useful business understanding. A four part organization introduces the material (Part I), describes and demonstrated basic data mining algorithms (Part II), focuses on the business applications of data mining (Part III), and presents an overview of the developing areas in this field, including web mining, text mining, and the ethical aspects of data mining. (Part IV). The author team has had extensive experience with the quantitative analysis of business as well as with data mining analysis. They have both taught this material and used their own graduate students to prepare the text’s data mining reports. Using real-world vignettes and their extensive knowledge of this new subject, David Olson and Yong Shi have created a text that demonstrates data mining processes and techniques needed for business applications.