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Author: ChengXiang Zhai Publisher: Morgan & Claypool ISBN: 1970001186 Category : Computers Languages : en Pages : 634
Book Description
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.
Author: ChengXiang Zhai Publisher: Morgan & Claypool ISBN: 1970001186 Category : Computers Languages : en Pages : 634
Book Description
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.
Author: Gary Miner Publisher: Academic Press ISBN: 012386979X Category : Computers Languages : en Pages : 1096
Book Description
"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"--
Author: Nicholas J. Horton Publisher: CRC Press ISBN: 1439827567 Category : Mathematics Languages : en Pages : 299
Book Description
Quick and Easy Access to Key Elements of Documentation Includes worked examples across a wide variety of applications, tasks, and graphicsUsing R for Data Management, Statistical Analysis, and Graphics presents an easy way to learn how to perform an analytical task in R, without having to navigate through the extensive, idiosyncratic, and sometimes
Author: Reda Alhajj Publisher: Springer Nature ISBN: 3030325873 Category : Computers Languages : en Pages : 261
Book Description
Data management and analysis is one of the fastest growing and most challenging areas of research and development in both academia and industry. Numerous types of applications and services have been studied and re-examined in this field resulting in this edited volume which includes chapters on effective approaches for dealing with the inherent complexity within data management and analysis. This edited volume contains practical case studies, and will appeal to students, researchers and professionals working in data management and analysis in the business, education, healthcare, and bioinformatics areas.
Author: Kristin Briney Publisher: Pelagic Publishing Ltd ISBN: 178427013X Category : Computers Languages : en Pages : 312
Book Description
A comprehensive guide to everything scientists need to know about data management, this book is essential for researchers who need to learn how to organize, document and take care of their own data. Researchers in all disciplines are faced with the challenge of managing the growing amounts of digital data that are the foundation of their research. Kristin Briney offers practical advice and clearly explains policies and principles, in an accessible and in-depth text that will allow researchers to understand and achieve the goal of better research data management. Data Management for Researchers includes sections on: * The data problem – an introduction to the growing importance and challenges of using digital data in research. Covers both the inherent problems with managing digital information, as well as how the research landscape is changing to give more value to research datasets and code. * The data lifecycle – a framework for data’s place within the research process and how data’s role is changing. Greater emphasis on data sharing and data reuse will not only change the way we conduct research but also how we manage research data. * Planning for data management – covers the many aspects of data management and how to put them together in a data management plan. This section also includes sample data management plans. * Documenting your data – an often overlooked part of the data management process, but one that is critical to good management; data without documentation are frequently unusable. * Organizing your data – explains how to keep your data in order using organizational systems and file naming conventions. This section also covers using a database to organize and analyze content. * Improving data analysis – covers managing information through the analysis process. This section starts by comparing the management of raw and analyzed data and then describes ways to make analysis easier, such as spreadsheet best practices. It also examines practices for research code, including version control systems. * Managing secure and private data – many researchers are dealing with data that require extra security. This section outlines what data falls into this category and some of the policies that apply, before addressing the best practices for keeping data secure. * Short-term storage – deals with the practical matters of storage and backup and covers the many options available. This section also goes through the best practices to insure that data are not lost. * Preserving and archiving your data – digital data can have a long life if properly cared for. This section covers managing data in the long term including choosing good file formats and media, as well as determining who will manage the data after the end of the project. * Sharing/publishing your data – addresses how to make data sharing across research groups easier, as well as how and why to publicly share data. This section covers intellectual property and licenses for datasets, before ending with the altmetrics that measure the impact of publicly shared data. * Reusing data – as more data are shared, it becomes possible to use outside data in your research. This chapter discusses strategies for finding datasets and lays out how to cite data once you have found it. This book is designed for active scientific researchers but it is useful for anyone who wants to get more from their data: academics, educators, professionals or anyone who teaches data management, sharing and preservation. "An excellent practical treatise on the art and practice of data management, this book is essential to any researcher, regardless of subject or discipline." —Robert Buntrock, Chemical Information Bulletin
Author: Dr. Goutam Chakraborty Publisher: SAS Institute ISBN: 1612907873 Category : Computers Languages : en Pages : 340
Book Description
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.
Author: Ken Kleinman Publisher: CRC Press ISBN: 1420070592 Category : Mathematics Languages : en Pages : 325
Book Description
An All-in-One Resource for Using SAS and R to Carry out Common TasksProvides a path between languages that is easier than reading complete documentationSAS and R: Data Management, Statistical Analysis, and Graphics presents an easy way to learn how to perform an analytical task in both SAS and R, without having to navigate through the extensive, id
Author: Gabe Ignatow Publisher: SAGE Publications ISBN: 150633699X Category : Computers Languages : en Pages : 345
Book Description
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.
Author: Singh, Manoj Kumar Publisher: IGI Global ISBN: 1522501835 Category : Computers Languages : en Pages : 345
Book Description
“Big data” has become a commonly used term to describe large-scale and complex data sets which are difficult to manage and analyze using standard data management methodologies. With applications across sectors and fields of study, the implementation and possible uses of big data are limitless. Effective Big Data Management and Opportunities for Implementation explores emerging research on the ever-growing field of big data and facilitates further knowledge development on methods for handling and interpreting large data sets. Providing multi-disciplinary perspectives fueled by international research, this publication is designed for use by data analysts, IT professionals, researchers, and graduate-level students interested in learning about the latest trends and concepts in big data.
Author: William H. Inmon Publisher: Pearson Education ISBN: 0132712911 Category : Business & Economics Languages : en Pages : 353
Book Description
The Definitive Guide to Unstructured Data Management and Analysis--From the World’s Leading Information Management Expert A wealth of invaluable information exists in unstructured textual form, but organizations have found it difficult or impossible to access and utilize it. This is changing rapidly: new approaches finally make it possible to glean useful knowledge from virtually any collection of unstructured data. William H. Inmon--the father of data warehousing--and Anthony Nesavich introduce the next data revolution: unstructured data management. Inmon and Nesavich cover all you need to know to make unstructured data work for your organization. You’ll learn how to bring it into your existing structured data environment, leverage existing analytical infrastructure, and implement textual analytic processing technologies to solve new problems and uncover new opportunities. Inmon and Nesavich introduce breakthrough techniques covered in no other book--including the powerful role of textual integration, new ways to integrate textual data into data warehouses, and new SQL techniques for reading and analyzing text. They also present five chapter-length, real-world case studies--demonstrating unstructured data at work in medical research, insurance, chemical manufacturing, contracting, and beyond. This book will be indispensable to every business and technical professional trying to make sense of a large body of unstructured text: managers, database designers, data modelers, DBAs, researchers, and end users alike. Coverage includes What unstructured data is, and how it differs from structured data First generation technology for handling unstructured data, from search engines to ECM--and its limitations Integrating text so it can be analyzed with a common, colloquial vocabulary: integration engines, ontologies, glossaries, and taxonomies Processing semistructured data: uncovering patterns, words, identifiers, and conflicts Novel processing opportunities that arise when text is freed from context Architecture and unstructured data: Data Warehousing 2.0 Building unstructured relational databases and linking them to structured data Visualizations and Self-Organizing Maps (SOMs), including Compudigm and Raptor solutions Capturing knowledge from spreadsheet data and email Implementing and managing metadata: data models, data quality, and more
Author: ChengXiang Zhai
Author: ChengXiang Zhai
Author: Gary Miner
Author: Nicholas J. Horton
Author: Reda Alhajj
Author: Kristin Briney
Author: Dr. Goutam Chakraborty
Author: Ken Kleinman
Author: Gabe Ignatow
Author: Singh, Manoj Kumar
Author: William H. Inmon