Description
Book SynopsisText 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 V
Table of Contents
Preface xi
Acknowledgments xiii
About the Authors xv
Introduction 1
Chapter 1 Text Mining and Text Analytics 3
Chapter 2 Text Analytics Process Overview 15
Chapter 3 Text Data Source Capture 33
Chapter 4 Document Content and Characterization 43
Chapter 5 Textual Abstraction: Latent Structure, Dimension Reduction 73
Chapter 6 Classification and Prediction 103
Chapter 7 Boolean Methods of Classification and Prediction 125
Chapter 8 Speech to Text 139
Appendix A Mood State Identification in Text 157
Appendix B A Design Approach to Characterizing Users Based on Audio Interactions on a Conversational AI Platform 175
Appendix C SAS Patents in Text Analytics 189
Glossary 197
Index 203
