Text As Data: Combining qualitative and quantitative algorithms within the SAS system for accurate, effective and understandable text analytics
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. Text as Data: Computational Methods of Understanding Written Expression Using SAS 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.
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.
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, Text as Data is an essential resource for SAS users and data scientists in any industry or academic application.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
BARRY DEVILLE 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.
GURPREET SINGH BAWA 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.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Hardback. Etat : New. Text As Data: Combining qualitative and quantitative algorithms within the SAS system for accurate, effective and understandable text analytics 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 RandD, social media marketing, customer care, and health services are implementing more comprehensive text-inclusive, analytics strategies. Text as Data: Computational Methods of Understanding Written Expression Using SAS 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. 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. Understand the role of linguistics, machine learning, and multiple data sources in the text analytics workflowUnderstand how a range of quantitative algorithms and data representations reflect contextual effects to shape meaning and understandingAccess online data and code repositories, videos, tutorials, and case studiesLearn how SAS extends quantitative algorithms to produce expanded text analytics capabilitiesRedefine text in terms of data for more accurate analysis 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, Text as Data is an essential resource for SAS users and data scientists in any industry or academic application. N° de réf. du vendeur LU-9781119487128
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Hardcover. Etat : new. Hardcover. Text As Data: Combining qualitative and quantitative algorithms within the SAS system for accurate, effective and understandable text analytics 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. Text as Data: Computational Methods of Understanding Written Expression Using SAS 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. 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. Understand the role of linguistics, machine learning, and multiple data sources in the text analytics workflowUnderstand how a range of quantitative algorithms and data representations reflect contextual effects to shape meaning and understandingAccess online data and code repositories, videos, tutorials, and case studiesLearn how SAS extends quantitative algorithms to produce expanded text analytics capabilitiesRedefine text in terms of data for more accurate analysis This book offers a thorough introduction to the framework and dynamics of text analyticsand the underlying principles at workand 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, Text as Data is an essential resource for SAS users and data scientists in any industry or academic application. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9781119487128
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