Text as Data - (Wiley and SAS Business) by Barry Deville & Gurpreet Singh Bawa (Hardcover)
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<p/><br></br><p><b> About the Book </b></p></br></br>"This book provides a high-level, integrated overview of text analytics and the role of SAS software in the ever-evolving information technology environment. The author uses his 18 years of experience to show the reader how using multiple sources of data and machine learning approaches can produce superior results. He emphasizes text as qualitative data in the context of traditional quantitative data analytics. The book begins with a description of the structure and dynamics of the text analytics process, before delving deeper into his combined linguistic + quantitative approach to analyzing data. Topics covered include numeric vs. textual data, expression parsing and detection, and text maps"--<p/><br></br><p><b> Book Synopsis </b></p></br></br><p><b>Text As Data: Combining qualitative and quantitative algorithms within the SAS system for accurate, effective and understandable text analytics</b></p> <p>The need for powerful, accurate and increasingly automatic text analysis software in modern information technology has dramatically increased. Fields as diverse as financial management, fraud and cybercrime prevention, Pharmaceutical R&D, social media marketing, customer care, and health services are implementing more comprehensive text-inclusive, analytics strategies. <i>Text as Data: Computational Methods of Understanding Written Expression Using SAS </i>presents an overview of text analytics and the critical role SAS software plays in combining linguistic and quantitative algorithms in the evolution of this dynamic field.</p> <p>Drawing on over two decades of experience in text analytics, authors Barry deVille and Gurpreet Singh Bawa examine the evolution of text mining and cloud-based solutions, and the development of SAS Visual Text Analytics. By integrating quantitative data and textual analysis with advanced computer learning principles, the authors demonstrate the combined advantages of SAS compared to standard approaches, and show how approaching text as qualitative data within a quantitative analytics framework produces more detailed, accurate, and explanatory results.</p> <ul> <li>Understand the role of linguistics, machine learning, and multiple data sources in the text analytics workflow</li> <li>Understand how a range of quantitative algorithms and data representations reflect contextual effects to shape meaning and understanding</li> <li>Access online data and code repositories, videos, tutorials, and case studies</li> <li>Learn how SAS extends quantitative algorithms to produce expanded text analytics capabilities</li> <li>Redefine text in terms of data for more accurate analysis</li> </ul> <p>This book offers a thorough introduction to the framework and dynamics of text analytics--and the underlying principles at work--and provides an in-depth examination of the interplay between qualitative-linguistic and quantitative, data-driven aspects of data analysis. The treatment begins with a discussion on expression parsing and detection and provides insight into the core principles and practices of text parsing, theme, and topic detection. It includes advanced topics such as contextual effects in numeric and textual data manipulation, fine-tuning text meaning and disambiguation. As the first resource to leverage the power of SAS for text analytics, <i>Text as Data</i> is an essential resource for SAS users and data scientists in any industry or academic application. </p><p/><br></br><p><b> From the Back Cover </b></p></br></br><p>Accurate, efficient, and automated text analysis is proving itself increasingly practical in a variety of fields, including financial management, law enforcement, and customer care. In <i>Text as Data: Computational Methods of Understanding Written Expression Using SAS</i>, expert data scientists Barry DeVille and Gurpreet Singh Bawa deliver a thorough introduction to the framework, dynamics, and underlying principles of text analytics.</p> <p>They describe the relationship between qualitative-linguistic and quantitative, data-driven aspects of data analysis. The authors also explore the concepts of expression parsing and detection before moving on to an examination of the core principles and practices of text parsing, theme, and topic detection. Advanced topics, like contextual effects in numeric and textual data manipulation, fine-tuning text meaning, and disambiguation, are also covered. <p>This work offers an overview of text analytics and the central role played by SAS software in combining linguistic and quantitative forms of analysis in this rapidly evolving field. Readers will discover an insightful examination of the evolution of text mining and cloud-based solutions, as well as the development of SAS Visual Text Analytics. <p><i>Text as Data</i> provides a compelling argument for the proposition that SAS incorporates superior solutions compared to standard approaches and why text should be treated as qualitative data within a quantitative analytics framework to produce detailed, accurate, and explanatory results. It also explains the role of linguistics, machine learning, and multiple data sources in a typical text analytics workflow. <p><i>Text as Data: Computational Methods of Understanding Written Expression Using SAS</i> is ideal for SAS users and data scientists in any industry or academic application. It's also an indispensable resource for anyone interested in text analytics generally.<p/><br></br><p><b> About the Author </b></p></br></br><p><b>BARRY DEVILLE</b> is a Data Scientist and Solutions Architect with 18 years of experience working at SAS. He led the development of the KnowledgeSEEKER decision tree package and has given workshops and tutorials on decision trees for Statistics Canada, the American Marketing Association, the IEEE, and the Direct Marketing Association.</p> <p><b>GURPREET SINGH BAWA</b> is the Data Science Senior Manager at Accenture PLC in India. He delivers advanced analytics solutions for global clients in a variety of corporate sectors.
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