Text as Data: Computational Methods of Understanding Written Expression Using SAS
- Length: 240 pages
- Edition: 1
- Language: English
- Publisher: Wiley
- Publication Date: 2021-10-05
- ISBN-10: 1119487129
- ISBN-13: 9781119487128
- Sales Rank: #509159 (See Top 100 Books)
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.
Cover Table of Contents Title Page Copyright Dedication Preface Acknowledgments About the Authors Introduction Chapter 1: Text Mining and Text Analytics BACKGROUND AND TERMINOLOGY TEXT ANALYTICS: WHAT IS IT? NOTES Chapter 2:Text Analytics Process Overview TEXT ANALYTICS PROCESSING PROCESS BUILDING BLOCKS PROCESS DESCRIPTION LINGUISTIC PROCESSING INTERNAL REPRESENTATION AND TEXT PRODUCTS NOTES Chapter 3:Text Data Source Capture TEXT MINING DATA SOURCE ASSEMBLY CONSUMING LINGUISTICS TEXT PRODUCTS NOTES Chapter 4:Document Content and Characterization AUTHORSHIP ANALYTICS: EARLY TEXT INDICATORS AND MEASURES A CASE STUDY IN GENDER DETECTION SUMMARIZATION AND DISCOURSE ANALYSIS FACT EXTRACTION CONCLUSION NOTES Chapter 5: Textual Abstraction: Latent Structure, Dimension Reduction TEXT MINING DATA SOURCE ASSEMBLY LATENT STRUCTURE AND DIMENSIONAL REDUCTION ROUGH MEANING – APPROXIMATION FOR SINGULAR VALUE DIMENSIONS CONCLUSION NOTES Chapter 6: Classification and Prediction USE CASE SCENARIO IDENTIFYING DRIVERS OF TEXTUAL CONSUMER FEEDBACK USING DISTANCE‐BASED CLUSTERING AND MATRIX FACTORIZATION NOTES Chapter 7: Boolean Methods of Classification and Prediction RULE‐BASED TEXT CLASSIFICATION AND PREDICTION EXAMPLE OF BOOLEAN RULES APPLIED TO TEXT MINING VACCINE DATA SUMMARY NOTES Chapter 8: Speech to Text INTRODUCTION PROCESSING AUDIO FEEDBACK FURTHER ANALYSIS: SENTIMENT AND LATENT TOPICS CONCLUSION NOTES Appendix A: Mood State Identification in Text ORIGINS OF MOOD STATE IDENTIFICATION NOTES Appendix B: A Design Approach to Characterizing Users Based on Audio Interactions on a Conversational AI Platform AUDIO‐BASED USER INTERACTION INFERENCE IMPLEMENTATION SCENARIO: VOICE‐BASED CONVERSATIONAL AI PLATFORM NOTE Appendix C: SAS Patents in Text Analytics Glossary Index End User License Agreement
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