Text as Data

Computational Methods of Understanding Written Expression Using SAS

Barry DeVille, Gurpreet Singh Bawa

• Publisher: Wiley
• Year: 2021
• ISBN:
  9781119487159
• Edition: 1
• Topic: Computer Science
• Subtopic: Data Processing
• Index: Computer Science

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.

CHAPTER 1
Text Mining and Text Analytics

This chapter describes some of the background and recent history of text analytics and provides real-world examples of how text analytics works and solves business problems. This treatment provides examples of common forms of text analytics and examples of solution approaches. The discussion ranges from a history of the analytical treatment of text expression up to the most recent developments and applications.

BACKGROUND AND TERMINOLOGY

The analysis of written and spoken expression has been developing as a computer application over several decades. Some of the earliest research in machine learning and artificial intelligence dealt with the problem of reading and interpreting text as well as in text translation (machine translation). These early activities gave rise to a field of computer science known as natural language processing (NLP). The recent rapid development of computer power – including processing power, large data, high bandwidth communication, and cloud-based, high-capacity computer memory – has provided a major new (and considerably broadened) emphasis on computerized text processing and text analysis.

TEXT ANALYTICS: WHAT IS IT?

Text processing and text analysis are components of the developing area of understanding written and spoken expression. Commonly occurring text documents – such as traditional newspapers, journals and periodicals, and, more recently, electronic documents, such as social media posts and emails – are forms of written expression. This active, multilayered area in current computer applications joins well-established, traditional fields such as linguistics and literary analysis to form the outline of the emerging field we call text analytics.

Current approaches to text analytics operate in two reinforcing directions that incorporate traditional forms of linguistic and literary analysis with a wide range of statistical, artificial intelligence (AI), and cognitive computing techniques to effectively process written and spoken expressions. The decoded expressions are used to drive a wide range of computer-mediated inference tasks that includes artificial intelligence, cognitive computing, and statistical inference. An everyday example is when we speak or type in a destination in order to receive an optimal driving route. Similarly, a call center agent might decipher multiple forms of common requests in order to construct the most effective solution approach.

Our treatment throughout the chapters to come includes examples of common forms of text analytics and examples of solution approaches. The discussion ranges from a history of the analytical treatment of text expression up to the most recent developments and applications. Since speech is quickly becoming an important form of u...

Table of contents

• Cover
• Table of Contents
• Title Page
• Copyright
• Dedication
• Preface
• Acknowledgments
• About the Authors
• Introduction
• Chapter 1: Text Mining and Text Analytics
• Chapter 2:Text Analytics Process Overview
• Chapter 3:Text Data Source Capture
• Chapter 4:Document Content and Characterization
• Chapter 5: Textual Abstraction: Latent Structure, Dimension Reduction
• Chapter 6: Classification and Prediction
• Chapter 7: Boolean Methods of Classification and Prediction
• Chapter 8: Speech to Text
• Appendix A: Mood State Identification in Text
• Appendix B: A Design Approach to Characterizing Users Based on Audio Interactions on a Conversational AI Platform
• Appendix C: SAS Patents in Text Analytics
• Glossary
• Index
• End User License Agreement