Practical Text Analytics Maximizing the Value of Text Data /

Practical Text Analytics Maximizing the Value of Text Data /

Bibliographic Details
Main Authors: Anandarajan, Murugan (Author), Hill, Chelsey (Author), Nolan, Thomas (Author)
Corporate Author: SpringerLink (Online service)
Summary:XXVIII, 285 p. 265 illus., 157 illus. in color.
text
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2019.
Edition:1st ed. 2019.
Series:Advances in Analytics and Data Science, 2
Subjects:
Quantitative research.
Business information services.
Statistics .
Data Analysis and Big Data.
Business Information Systems.
Statistics in Business, Management, Economics, Finance, Insurance.
Online Access:https://doi.org/10.1007/978-3-319-95663-3
Format: Electronic Book
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.

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