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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| Main Authors: | , |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
Newark
John Wiley & Sons, Incorporated
2021
Wiley-Blackwell John Wiley & Sons (US) |
| Edition: | 1 |
| Series: | Wiley & SAS Business Series |
| Subjects: | |
| ISBN: | 1119487129, 9781119487128 |
| Online Access: | Get full text |
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Table of Contents:
- 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