Practical Text Analytics Maximizing the Value of Text Data /

• Main Authors: Anandarajan, Murugan (Author, http://id.loc.gov/vocabulary/relators/aut), Hill, Chelsey (http://id.loc.gov/vocabulary/relators/aut), Nolan, Thomas (http://id.loc.gov/vocabulary/relators/aut)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Edition: 1st ed. 2019.
• Series: Advances in Analytics and Data Science, 2
• Subjects: Big data.; Management information systems.; Statistics .; Big Data/Analytics.; Business Information Systems.; Statistics for Business, Management, Economics, Finance, Insurance.

This book introduces text analytics as a valuable method for deriving insights from text data. Unlike other text analytics publications, Practical Text Analytics: Maximizing the Value of Text Data makes technical concepts accessible to those without extensive experience in the field. Using text anal...

Table of Contents:

• Chapter 1. Introduction to Text Analytics
• Chapter 2. Fundamentals of Content Analysis
• Chapter 3. Text Analytics Roadmap
• Chapter 4. Text Pre-Processing
• Chapter 5. Term-Document Representation
• Chapter 6. Semantic Space Representation and Latent Semantic Analysis
• Chapter 7. Cluster Analysis: Modeling Groups in Text
• Chapter 8. Probabilistic Topic Models
• Chapter 9. Classification Analysis: Machine Learning Applied to Text
• Chapter 10. Modeling Text Sentiment: Learning and Lexicon Models
• Chapter 11. Storytelling Using Text Data
• Chapter 12. Visualizing Results
• Chapter 13. Sentiment Analysis of Movie Reviews using R
• Chapter 14. Latent Semantic Analysis (LSA) in Python
• Chapter 15. Learning-Based Sentiment Analysis using RapidMiner
• Chapter 16. SAS Visual Text Analytics.