Text As Data Computational Methods of Understanding Written Expression Using SAS
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 div...
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| Médium: | E-kniha |
| Jazyk: | angličtina |
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Newark
John Wiley & Sons, Incorporated
2021
Wiley-Blackwell John Wiley & Sons (US) |
| Vydání: | 1 |
| Edice: | Wiley & SAS Business Series |
| Témata: | |
| ISBN: | 1119487129, 9781119487128 |
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| Abstract | 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 workflow Understand how a range of quantitative algorithms and data representations reflect contextual effects to shape meaning and understanding Access online data and code repositories, videos, tutorials, and case studies Learn how SAS extends quantitative algorithms to produce expanded text analytics capabilities Redefine 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. |
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| AbstractList | 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 workflow Understand how a range of quantitative algorithms and data representations reflect contextual effects to shape meaning and understanding Access online data and code repositories, videos, tutorials, and case studies Learn how SAS extends quantitative algorithms to produce expanded text analytics capabilities Redefine 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. 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. -- |
| Author | Singh Bawa, Gurpreet DeVille, Barry |
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| DOI | 10.1002/9781119487142 |
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| Publisher | John Wiley & Sons, Incorporated Wiley-Blackwell John Wiley & Sons (US) |
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| Snippet | Text As Data: Combining qualitative and quantitative algorithms within the SAS system for accurate, effective and understandable text analytics The need for... This book offers a thorough introduction to the framework and dynamics of text analyticsand the underlying principles at workand provides an in-depth... |
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| SubjectTerms | Computational intelligence Text data mining |
| SubjectTermsDisplay | Computational intelligence. Electronic books. Text data mining. |
| Subtitle | Computational Methods of Understanding Written Expression Using SAS |
| TableOfContents | Latent Structure: Tracking Topic Term Variability Across Semantic Fields -- Conclusion -- Notes -- Chapter 6 Classification and Prediction -- Use Case Scenario -- Composite Document Construction -- Model Development -- Ensemble or Multiagent Models -- Identifying Drivers of Textual Consumer Feedback Using Distance-Based Clustering and Matrix Factorization -- Use Case Scenario: Retailer Reliability Ecommerce -- Discussion -- Notes -- Chapter 7 Boolean Methods of Classification and Prediction -- Rule-Based Text Classification and Prediction -- Method Description -- Characteristics of Boolean Rule Methods -- Example of Boolean Rules Applied to Text Mining Vaccine Data -- An Example Analysis -- Summary -- Notes -- Chapter 8 Speech to Text -- Introduction -- Processing Audio Feedback -- Business Problem -- Process Components -- Further Analysis: Sentiment and Latent Topics -- Conclusion -- Notes -- Appendix A Mood State Identification in Text -- Origins of Mood State Identification -- An Approach to Mood State Developed at SAS -- Background and Discussion -- An Example Mood State Process Flow -- Notes -- Appendix B A Design Approach to Characterizing Users Based on AudioInteractions on a Conversational AI Platform -- Audio-Based User Interaction Inference -- Recommendation Perspective vs. Conventional -- Sole Dependency on Text-Based Bots -- Implementation Scenario: Voice-Based Conversational AI Platform -- Component Process Flow -- Constructed Interaction -- Note -- Appendix C SAS Patents in Text Analytics -- Glossary -- Index -- EULA Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgments -- About the Authors -- Introduction -- Chapter 1 Text Mining and Text Analytics -- Background and Terminology -- Text Analytics: What Is It? -- Brief History of Text -- Writing Systems of the World -- Meaning and Ambiguity -- Notes -- Chapter 2 Text Analytics Process Overview -- Text Analytics Processing -- Process Building Blocks -- Preparation -- Utilization -- Process Description -- Text Mining Data Sources -- Capture -- Linguistic Processing -- Parsing and Parse Products -- Internal Representation and Text Products -- Representation -- Notes -- Chapter 3 Text Data Source Capture -- Text Mining Data Source Assembly -- Use Case: Accessing Text from SAS Conference Proceedings -- Text Data Capture Process -- Consuming Linguistics Text Products -- Notes -- Chapter 4 Document Content and Characterization -- Authorship Analytics: Early Text Indicators and Measures -- Function Words as Indicators -- Beyond Function Words -- Words and Word Forms as Psychological Artifacts -- A Case Study in Gender Detection -- Data Product Example -- Analysis Results -- Summarization and Discourse Analysis -- Elementary Operations as Building Blocks to Results -- Fact Extraction -- Sentiment Extraction -- Conditional Inference -- Deployment -- Summarization -- Conclusion -- Notes -- Chapter 5 Textual Abstraction: Latent Structure, Dimension Reduction -- Text Mining Data Source Assembly -- Latent Structure and Dimensional Reduction -- Singular Value Decomposition as Dimension Reduction -- Latent Semantic Analysis -- Clustering Approach to Document Classification -- SVD Approach to Document Indexing -- Rough Meaning - Approximation for Singular Value Dimensions -- Semantic Indexing: Assigning Category Based on Singular Value Dimensional Scores -- Identifying Topics Using Latent Structure |
| Title | Text As Data |
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