Search Results for mining-text-data

This book contains a wide swath in topics across social networks & data mining. Each chapter contains a comprehensive survey including the key research content on the topic, and the future directions of research in the field.

Author: Charu C. Aggarwal

Publisher: Springer Science & Business Media

ISBN: 9781461432234

Category: Computers

Page: 524

View: 845

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Text mining applications have experienced tremendous advances because of web 2.0 and social networking applications. Recent advances in hardware and software technology have lead to a number of unique scenarios where text mining algorithms are learned. Mining Text Data introduces an important niche in the text analytics field, and is an edited volume contributed by leading international researchers and practitioners focused on social networks & data mining. This book contains a wide swath in topics across social networks & data mining. Each chapter contains a comprehensive survey including the key research content on the topic, and the future directions of research in the field. There is a special focus on Text Embedded with Heterogeneous and Multimedia Data which makes the mining process much more challenging. A number of methods have been designed such as transfer learning and cross-lingual mining for such cases. Mining Text Data simplifies the content, so that advanced-level students, practitioners and researchers in computer science can benefit from this book. Academic and corporate libraries, as well as ACM, IEEE, and Management Science focused on information security, electronic commerce, databases, data mining, machine learning, and statistics are the primary buyers for this reference book.
2012-02-03 By Charu C. Aggarwal

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 ...

Author: Gary Miner

Publisher: Academic Press

ISBN: 9780123870117

Category: Mathematics

Page: 1000

View: 479

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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 possible Numerous examples, tutorials, power points and datasets available via companion website on Elsevierdirect.com Glossary of text mining terms provided in the appendix
2012-01-25 By Gary Miner

Serving also as a practical guide, this unique book provides helpful advice illustrated by examples and case studies.

Author: Sholom M. Weiss

Publisher: Springer

ISBN: 9781447167501

Category: Computers

Page: 239

View: 507

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This successful textbook on predictive text mining offers a unified perspective on a rapidly evolving field, integrating topics spanning the varied disciplines of data science, machine learning, databases, and computational linguistics. Serving also as a practical guide, this unique book provides helpful advice illustrated by examples and case studies. This highly anticipated second edition has been thoroughly revised and expanded with new material on deep learning, graph models, mining social media, errors and pitfalls in big data evaluation, Twitter sentiment analysis, and dependency parsing discussion. The fully updated content also features in-depth discussions on issues of document classification, information retrieval, clustering and organizing documents, information extraction, web-based data-sourcing, and prediction and evaluation. Features: includes chapter summaries and exercises; explores the application of each method; provides several case studies; contains links to free text-mining software.
2015-09-07 By Sholom M. Weiss

Author: A. Zanasi

Publisher:

ISBN: OCLC:803691682

Category:

Page: 434

View: 680

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2004 By A. Zanasi

This Second Edition brings readers thoroughly up to date with the emerging field of text mining, the application of techniques of machine learning in conjunction with natural language processing, information extraction, and ...

Author: Michael W. Berry

Publisher: Springer

ISBN: 184996713X

Category: Computers

Page: 240

View: 476

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This Second Edition brings readers thoroughly up to date with the emerging field of text mining, the application of techniques of machine learning in conjunction with natural language processing, information extraction, and algebraic/mathematical approaches to computational information retrieval. The book explores a broad range of issues, ranging from the development of new learning approaches to the parallelization of existing algorithms. Authors highlight open research questions in document categorization, clustering, and trend detection. In addition, the book describes new application problems in areas such as email surveillance and anomaly detection.
2010-10-13 By Michael W. Berry

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 ...

Author: Domenica Fioredistella Iezzi

Publisher: Springer Nature

ISBN: 9783030526801

Category: Social Science

Page: 302

View: 603

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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.

Author:

Publisher:

ISBN: OCLC:1066443397

Category: Data mining

Page:

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2004 By

This book covers general computational techniques for managing and analyzing large amounts of text data that can help users manage and make use of text data in all kinds of applications.

Author: ChengXiang Zhai

Publisher:

ISBN: 1970001194

Category: Computational linguistics

Page: 510

View: 597

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The growth of "big data" created unprecedented opportunities to leverage computational and statistical approaches to turn raw data into actionable knowledge that can support various application tasks. This is especially true for the optimization of decision making in virtually all application domains such as health and medicine, security and safety, learning and education, scientific discovery, and business intelligence. Just as a microscope enables us to see things in the "micro world" and a telescope allows us to see things far away, one can imagine a "big data scope" would enable us to extend our perception ability to "see" useful hidden information and knowledge buried in the data, which can help make predictions and improve the optimality of a chosen decision. This book covers general computational techniques for managing and analyzing large amounts of text data that can help users manage and make use of text data in all kinds of applications.
2016 By ChengXiang Zhai

However, acquiring such multidimensional knowledge from massive text data remains a challenging task. This book presents data mining techniques that turn unstructured text data into multidimensional knowledge.

Author: Chao Zhang

Publisher: Morgan & Claypool Publishers

ISBN: 9781681735207

Category: Computers

Page: 198

View: 370

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Unstructured text, as one of the most important data forms, plays a crucial role in data-driven decision making in domains ranging from social networking and information retrieval to scientific research and healthcare informatics. In many emerging applications, people's information need from text data is becoming multidimensional—they demand useful insights along multiple aspects from a text corpus. However, acquiring such multidimensional knowledge from massive text data remains a challenging task. This book presents data mining techniques that turn unstructured text data into multidimensional knowledge. We investigate two core questions. (1) How does one identify task-relevant text data with declarative queries in multiple dimensions? (2) How does one distill knowledge from text data in a multidimensional space? To address the above questions, we develop a text cube framework. First, we develop a cube construction module that organizes unstructured data into a cube structure, by discovering latent multidimensional and multi-granular structure from the unstructured text corpus and allocating documents into the structure. Second, we develop a cube exploitation module that models multiple dimensions in the cube space, thereby distilling from user-selected data multidimensional knowledge. Together, these two modules constitute an integrated pipeline: leveraging the cube structure, users can perform multidimensional, multigranular data selection with declarative queries; and with cube exploitation algorithms, users can extract multidimensional patterns from the selected data for decision making. The proposed framework has two distinctive advantages when turning text data into multidimensional knowledge: flexibility and label-efficiency. First, it enables acquiring multidimensional knowledge flexibly, as the cube structure allows users to easily identify task-relevant data along multiple dimensions at varied granularities and further distill multidimensional knowledge. Second, the algorithms for cube construction and exploitation require little supervision; this makes the framework appealing for many applications where labeled data are expensive to obtain.
2019-03-21 By Chao Zhang

This book contains most of the papers presented at the Sixth International Conference on Data Mining held in Skiathos, Greece.

Author: A. Zanasi

Publisher: Wit Pr/Computational Mechanics

ISBN: UOM:39015061427228

Category: Computers

Page: 550

View: 931

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This book contains most of the papers presented at the Sixth International Conference on Data Mining held in Skiathos, Greece. Twenty-five countries from all the continents are represented in the papers published in the book, offering a real multinational and multicultural range of experiences and ideas.
2005 By A. Zanasi

This book discusses various aspects of text data mining. Unlike other books that focus on machine learning or databases, it approaches text data mining from a natural language processing (NLP) perspective.

Author: Chengqing Zong

Publisher: Springer Nature

ISBN: 9789811601002

Category:

Page:

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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.

Author: ChengXiang Zhai

Publisher: Morgan & Claypool

ISBN: 9781970001181

Category: Computers

Page: 530

View: 995

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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.
2016-06-30 By ChengXiang Zhai

This book takes a practical, hands-on approach to teaching you a reliable, cost-effective approach to mining the vast, untold riches buried within all forms of text using R. Author Ted Kwartler clearly describes all of the tools needed to ...

Author: Ted Kwartler

Publisher: John Wiley & Sons

ISBN: 9781119282013

Category: Mathematics

Page: 320

View: 781

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A reliable, cost-effective approach to extracting priceless business information from all sources of text Excavating actionable business insights from data is a complex undertaking, and that complexity is magnified by an order of magnitude when the focus is on documents and other text information. This book takes a practical, hands-on approach to teaching you a reliable, cost-effective approach to mining the vast, untold riches buried within all forms of text using R. Author Ted Kwartler clearly describes all of the tools needed to perform text mining and shows you how to use them to identify practical business applications to get your creative text mining efforts started right away. With the help of numerous real-world examples and case studies from industries ranging from healthcare to entertainment to telecommunications, he demonstrates how to execute an array of text mining processes and functions, including sentiment scoring, topic modelling, predictive modelling, extracting clickbait from headlines, and more. You’ll learn how to: Identify actionable social media posts to improve customer service Use text mining in HR to identify candidate perceptions of an organisation, match job descriptions with resumes, and more Extract priceless information from virtually all digital and print sources, including the news media, social media sites, PDFs, and even JPEG and GIF image files Make text mining an integral component of marketing in order to identify brand evangelists, impact customer propensity modelling, and much more Most companies’ data mining efforts focus almost exclusively on numerical and categorical data, while text remains a largely untapped resource. Especially in a global marketplace where being first to identify and respond to customer needs and expectations imparts an unbeatable competitive advantage, text represents a source of immense potential value. Unfortunately, there is no reliable, cost-effective technology for extracting analytical insights from the huge and ever-growing volume of text available online and other digital sources, as well as from paper documents—until now.
2017-07-24 By Ted Kwartler

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.

Author: Gabe Ignatow

Publisher: SAGE Publications

ISBN: 9781506337029

Category: Social Science

Page: 344

View: 998

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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.
2017-09-22 By Gabe Ignatow

This book discusses various aspects of text data mining. Unlike other books that focus on machine learning or databases, it approaches text data mining from a natural language processing (NLP) perspective.

Author: Chengqing Zong

Publisher: Springer

ISBN: 9811600996

Category: Computers

Page: 351

View: 870

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This book discusses various aspects of text data mining. Unlike other books that focus on machine learning or databases, it approaches text data mining from a natural language processing (NLP) perspective. The book offers a detailed introduction to the fundamental theories and methods of text data mining, ranging from pre-processing (for both Chinese and English texts), text representation and feature selection, to text classification and text clustering. It also presents the predominant applications of text data mining, for example, topic modeling, sentiment analysis and opinion mining, topic detection and tracking, information extraction, and automatic text summarization. Bringing all the related concepts and algorithms together, it offers a comprehensive, authoritative and coherent overview. Written by three leading experts, it is valuable both as a textbook and as a reference resource for students, researchers and practitioners interested in text data mining. It can also be used for classes on text data mining or NLP.
2021-05-23 By Chengqing Zong

Natural Language Processing and Text Mining not only discusses applications of Natural Language Processing techniques to certain Text Mining tasks, but also the converse, the use of Text Mining to assist NLP.

Author: Anne Kao

Publisher: Springer Science & Business Media

ISBN: 9781846287541

Category: Computers

Page: 265

View: 259

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Natural Language Processing and Text Mining not only discusses applications of Natural Language Processing techniques to certain Text Mining tasks, but also the converse, the use of Text Mining to assist NLP. It assembles a diverse views from internationally recognized researchers and emphasizes caveats in the attempt to apply Natural Language Processing to text mining. This state-of-the-art survey is a must-have for advanced students, professionals, and researchers.
2007-03-06 By Anne Kao

Emphasizing predictive methods, the book unifies all key areas in text mining: preprocessing, text categorization, information search and retrieval, clustering of documents, and information extraction.

Author: Sholom M. Weiss

Publisher: Springer Science & Business Media

ISBN: 9780387345550

Category: Computers

Page: 237

View: 766

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Data mining is a mature technology. The prediction problem, looking for predictive patterns in data, has been widely studied. Strong me- ods are available to the practitioner. These methods process structured numerical information, where uniform measurements are taken over a sample of data. Text is often described as unstructured information. So, it would seem, text and numerical data are different, requiring different methods. Or are they? In our view, a prediction problem can be solved by the same methods, whether the data are structured - merical measurements or unstructured text. Text and documents can be transformed into measured values, such as the presence or absence of words, and the same methods that have proven successful for pred- tive data mining can be applied to text. Yet, there are key differences. Evaluation techniques must be adapted to the chronological order of publication and to alternative measures of error. Because the data are documents, more specialized analytical methods may be preferred for text. Moreover, the methods must be modi?ed to accommodate very high dimensions: tens of thousands of words and documents. Still, the central themes are similar.
2010-01-08 By Sholom M. Weiss

This Second Edition brings readers thoroughly up to date with the emerging field of text mining, the application of techniques of machine learning in conjunction with natural language processing, information extraction, and ...

Author: Michael W. Berry

Publisher: Springer Science & Business Media

ISBN: 9781848000469

Category: Computers

Page: 240

View: 903

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This Second Edition brings readers thoroughly up to date with the emerging field of text mining, the application of techniques of machine learning in conjunction with natural language processing, information extraction, and algebraic/mathematical approaches to computational information retrieval. The book explores a broad range of issues, ranging from the development of new learning approaches to the parallelization of existing algorithms. Authors highlight open research questions in document categorization, clustering, and trend detection. In addition, the book describes new application problems in areas such as email surveillance and anomaly detection.
2007-12-10 By Michael W. Berry

This book constitutes the refereed proceedings of the 11th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2015, held in Hamburg, Germany in July 2015.

Author: Petra Perner

Publisher: Springer

ISBN: 3319210238

Category: Computers

Page: 454

View: 213

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This book constitutes the refereed proceedings of the 11th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2015, held in Hamburg, Germany in July 2015. The 41 full papers presented were carefully reviewed and selected from 123 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image mining, text mining, video mining and Web mining.
2015-07-13 By Petra Perner

This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions.

Author: Robert Nisbet

Publisher: Elsevier

ISBN: 9780124166455

Category: Mathematics

Page: 822

View: 261

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Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce. Includes input by practitioners for practitioners Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models Contains practical advice from successful real-world implementations Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications
2017-11-09 By Robert Nisbet

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