Text Data Management And Analysis

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Text Data Management And Analysis

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Author : ChengXiang Zhai
language : en
Publisher: Morgan & Claypool
Release Date : 2016-06-30

Text Data Management And Analysis written by ChengXiang Zhai and has been published by Morgan & Claypool this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-30 with Computers categories.

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.

An Introduction To Text Mining

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Author : Gabe Ignatow
language : en
Publisher: SAGE Publications
Release Date : 2017-09-22

An Introduction To Text Mining written by Gabe Ignatow and has been published by SAGE Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-22 with Social Science categories.

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.

Practical Text Mining And Statistical Analysis For Non Structured Text Data Applications

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Author : Gary D. Miner
language : en
Publisher: Academic Press
Release Date : 2012-01-25

Practical Text Mining And Statistical Analysis For Non Structured Text Data Applications written by Gary D. Miner and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-01-25 with Mathematics categories.

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

Qualitative Text Analysis

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Author : Udo Kuckartz
language : en
Publisher: SAGE
Release Date : 2014-01-23

Qualitative Text Analysis written by Udo Kuckartz and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-01-23 with Reference categories.

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.

Database Management Using Ai A Comprehensive Guide

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Author : A Purushotham Reddy
language : en
Publisher: A Purushotham Reddy
Release Date : 2024-10-20

Database Management Using Ai A Comprehensive Guide written by A Purushotham Reddy and has been published by A Purushotham Reddy this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-20 with Computers categories.

Database Management Using AI: The Ultimate Guide for Data Professionals Database Management Using AI: A Comprehensive Guide is an essential resource for anyone eager to explore how artificial intelligence (AI) is revolutionizing the field of database management. This book caters to a wide audience, from database administrators, data scientists, and tech enthusiasts to professionals looking to integrate AI into their data management practices. It offers a professional yet easily understandable exploration of how AI is transforming modern data systems. The guide starts by laying a solid foundation in database management fundamentals, covering key concepts such as data models, SQL, and database design principles. It then delves into how AI can optimize database performance, enhance security, and automate complex tasks like data retrieval, query optimization, and schema design. With this book, readers will gain deep insights into integrating AI with traditional database systems and how AI tools are shaping the future of data management. Unlike other books that focus purely on theory, this guide stands out by emphasizing real-world applications. Through practical case studies, it demonstrates how AI-driven database systems are being leveraged across industries such as e-commerce, healthcare, finance, and logistics. These case studies show the real-world impact of AI, helping businesses increase efficiency, reduce errors, and make smarter, data-backed decisions. The book illustrates how AI is enabling organizations to stay ahead in a competitive market by harnessing the power of intelligent database management. Throughout the guide, readers will learn about the evolution of database systems, including the shift from relational databases to modern NoSQL databases, and how AI is enhancing traditional database models to meet the demands of the digital age. The book explores how AI integration in databases is transforming how data is processed and analyzed, automating repetitive tasks and improving the scalability and performance of databases. One of the key highlights of this book is the coverage of AI in database management. Readers will learn how AI is being used to automate routine database tasks, improve security by predicting and mitigating threats, and streamline database management operations through automation. Additionally, the book delves into how AI helps in predictive analytics and data mining, uncovering hidden patterns and enabling organizations to make accurate predictions based on large volumes of data. The book also covers predictive analytics and data mining, teaching readers how AI tools can be used to extract valuable insights from data, identify trends, and uncover business opportunities that were previously hard to detect. By understanding how AI can leverage data to drive business intelligence, readers will be able to implement AI-driven solutions that improve decision-making processes. Furthermore, this guide explores the future of database management with AI. It takes a close look at emerging trends, including autonomous databases and the growing role of cloud-based AI solutions in shaping the future of data management. These innovative technologies are creating intelligent, self-managing databases that are poised to revolutionize how data is stored, processed, and analyzed. Database Management Using AI provides readers with the knowledge and practical skills needed to navigate the fast-evolving landscape of AI-powered databases. Whether you're an industry professional or a student, this book is packed with actionable insights that will keep you ahead in the digital world. It’s a must-have resource for anyone looking to understand the practical impact of AI on database systems and harness the power of machine learning, big data, and cloud computing to transform their approach to data management. With its combination of clear explanations, real-world case studies, and forward-looking insights, this book is the ultimate guide for anyone wanting to stay competitive in the digital age. Database Management Using AI is more than just a book—it's an essential tool for anyone serious about mastering the future of data systems. Refer www.latest2all.com for details...

Practical Text Analytics

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Author : Murugan Anandarajan
language : en
Publisher: Springer
Release Date : 2018-10-19

Practical Text Analytics written by Murugan Anandarajan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-19 with Business & Economics categories.

This book introduces text analytics as a valuable method for deriving insights from text data. Unlike other text analytics publications, Practical Text Analytics: Maximizing the Value of Text Data makes technical concepts accessible to those without extensive experience in the field. Using text analytics, organizations can derive insights from content such as emails, documents, and social media. Practical Text Analytics is divided into five parts. The first part introduces text analytics, discusses the relationship with content analysis, and provides a general overview of text mining methodology. In the second part, the authors discuss the practice of text analytics, including data preparation and the overall planning process. The third part covers text analytics techniques such as cluster analysis, topic models, and machine learning. In the fourth part of the book, readers learn about techniques used to communicate insights from text analysis, including data storytelling. The final part of Practical Text Analytics offers examples of the application of software programs for text analytics, enabling readers to mine their own text data to uncover information.

Effective Big Data Management And Opportunities For Implementation

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Author : Singh, Manoj Kumar
language : en
Publisher: IGI Global
Release Date : 2016-06-20

Effective Big Data Management And Opportunities For Implementation written by Singh, Manoj Kumar and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-20 with Computers categories.

“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.

Data Management Analytics And Innovation

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Author : Valentina Emilia Balas
language : en
Publisher: Springer
Release Date : 2018-09-07

Data Management Analytics And Innovation written by Valentina Emilia Balas and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-07 with Technology & Engineering categories.

The volume on Data Management, Analytics and Innovations presents the latest high-quality technical contributions and research results in the areas of data management and smart computing, big data management, artificial intelligence and data analytics along with advances in network technologies. It deals with the state-of-the-art topics and provides challenges and solutions for future development. Original, unpublished research work highlighting specific research domains from all viewpoints are contributed from scientists throughout the globe. This volume is mainly designed for professional audience, composed of researchers and practitioners in academia and industry.

Data Management For Social Scientists

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Author : Nils B. Weidmann
language : en
Publisher: Cambridge University Press
Release Date : 2023-03-09

Data Management For Social Scientists written by Nils B. Weidmann and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-09 with Social Science categories.

Equips social scientists with the tools and techniques to conduct quantitative research in the age of big data.

Applied Text Analysis With Python

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Author : Benjamin Bengfort
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2018-06-11

Applied Text Analysis With Python written by Benjamin Bengfort and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-11 with Computers categories.

From news and speeches to informal chatter on social media, natural language is one of the richest and most underutilized sources of data. Not only does it come in a constant stream, always changing and adapting in context; it also contains information that is not conveyed by traditional data sources. The key to unlocking natural language is through the creative application of text analytics. This practical book presents a data scientist’s approach to building language-aware products with applied machine learning. You’ll learn robust, repeatable, and scalable techniques for text analysis with Python, including contextual and linguistic feature engineering, vectorization, classification, topic modeling, entity resolution, graph analysis, and visual steering. By the end of the book, you’ll be equipped with practical methods to solve any number of complex real-world problems. Preprocess and vectorize text into high-dimensional feature representations Perform document classification and topic modeling Steer the model selection process with visual diagnostics Extract key phrases, named entities, and graph structures to reason about data in text Build a dialog framework to enable chatbots and language-driven interaction Use Spark to scale processing power and neural networks to scale model complexity