Text as data computational methods of understanding written expression using SAS
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. -- Edited summary from book
| Otros Autores: | , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Hoboken, NJ :
John Wiley & Sons, Inc
[2022]
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| Colección: | Wiley and SAS business series.
|
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009645696406719 |
Tabla de Contenidos:
- 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.
- 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.
