Text Mining and Visualization

Text Mining and Visualization

by Markus Hofmann, Andrew Chisholm
Released January 2016
Publisher(s): Chapman and Hall/CRC
ISBN: 9781482237580

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Book description

This book provides an introduction to text mining using some of the most popular and powerful open-source tools: KNIME, RapidMiner, Weka, R, and Python. The contributors explain how text data are gathered and processed from a wide variety of sources, including books, server access logs, websites, social media sites, and message boards. Each chapter presents a case study that readers can follow as part of a step-by-step, reproducible example. The examples used are available on a supplementary website.

Table of contents

  1. Front Cover (1/2)
  2. Front Cover (2/2)
  3. Contents (1/7)
  4. Contents (2/7)
  5. Contents (3/7)
  6. Contents (4/7)
  7. Contents (5/7)
  8. Contents (6/7)
  9. Contents (7/7)
  10. I: RapidMiner
    1. 1. RapidMiner for Text Analytic Fundamentals (1/7)
    2. 1. RapidMiner for Text Analytic Fundamentals (2/7)
    3. 1. RapidMiner for Text Analytic Fundamentals (3/7)
    4. 1. RapidMiner for Text Analytic Fundamentals (4/7)
    5. 1. RapidMiner for Text Analytic Fundamentals (5/7)
    6. 1. RapidMiner for Text Analytic Fundamentals (6/7)
    7. 1. RapidMiner for Text Analytic Fundamentals (7/7)
    8. 2. Empirical Zipf-Mandelbrot Variation for Sequential Windows within Documents (1/5)
    9. 2. Empirical Zipf-Mandelbrot Variation for Sequential Windows within Documents (2/5)
    10. 2. Empirical Zipf-Mandelbrot Variation for Sequential Windows within Documents (3/5)
    11. 2. Empirical Zipf-Mandelbrot Variation for Sequential Windows within Documents (4/5)
    12. 2. Empirical Zipf-Mandelbrot Variation for Sequential Windows within Documents (5/5)
  11. II: KNIME
    1. 3. Introduction to the KNIME Text Processing Extension (1/4)
    2. 3. Introduction to the KNIME Text Processing Extension (2/4)
    3. 3. Introduction to the KNIME Text Processing Extension (3/4)
    4. 3. Introduction to the KNIME Text Processing Extension (4/4)
    5. 4. Social Media Analysis – Text Mining Meets Network Mining (1/3)
    6. 4. Social Media Analysis – Text Mining Meets Network Mining (2/3)
    7. 4. Social Media Analysis – Text Mining Meets Network Mining (3/3)
  12. III: Python
    1. 5. Mining Unstructured User Reviews with Python (1/8)
    2. 5. Mining Unstructured User Reviews with Python (2/8)
    3. 5. Mining Unstructured User Reviews with Python (3/8)
    4. 5. Mining Unstructured User Reviews with Python (4/8)
    5. 5. Mining Unstructured User Reviews with Python (5/8)
    6. 5. Mining Unstructured User Reviews with Python (6/8)
    7. 5. Mining Unstructured User Reviews with Python (7/8)
    8. 5. Mining Unstructured User Reviews with Python (8/8)
    9. 6. Sentiment Classification and Visualization of Product Review Data (1/4)
    10. 6. Sentiment Classification and Visualization of Product Review Data (2/4)
    11. 6. Sentiment Classification and Visualization of Product Review Data (3/4)
    12. 6. Sentiment Classification and Visualization of Product Review Data (4/4)
    13. 7. Mining Search Logs for Usage Patterns (1/4)
    14. 7. Mining Search Logs for Usage Patterns (2/4)
    15. 7. Mining Search Logs for Usage Patterns (3/4)
    16. 7. Mining Search Logs for Usage Patterns (4/4)
    17. 8. Temporally Aware Online News Mining and Visualization with Python (1/6)
    18. 8. Temporally Aware Online News Mining and Visualization with Python (2/6)
    19. 8. Temporally Aware Online News Mining and Visualization with Python (3/6)
    20. 8. Temporally Aware Online News Mining and Visualization with Python (4/6)
    21. 8. Temporally Aware Online News Mining and Visualization with Python (5/6)
    22. 8. Temporally Aware Online News Mining and Visualization with Python (6/6)
    23. 9. Text Classification Using Python (1/5)
    24. 9. Text Classification Using Python (2/5)
    25. 9. Text Classification Using Python (3/5)
    26. 9. Text Classification Using Python (4/5)
    27. 9. Text Classification Using Python (5/5)
  13. IV: R
    1. 10. Sentiment Analysis of Stock Market Behavior from Twitter Using the R Tool (1/4)
    2. 10. Sentiment Analysis of Stock Market Behavior from Twitter Using the R Tool (2/4)
    3. 10. Sentiment Analysis of Stock Market Behavior from Twitter Using the R Tool (3/4)
    4. 10. Sentiment Analysis of Stock Market Behavior from Twitter Using the R Tool (4/4)
    5. 11. Topic Modeling (1/5)
    6. 11. Topic Modeling (2/5)
    7. 11. Topic Modeling (3/5)
    8. 11. Topic Modeling (4/5)
    9. 11. Topic Modeling (5/5)
    10. 12. Empirical Analysis of the Stack Overflow Tags Network (1/7)
    11. 12. Empirical Analysis of the Stack Overflow Tags Network (2/7)
    12. 12. Empirical Analysis of the Stack Overflow Tags Network (3/7)
    13. 12. Empirical Analysis of the Stack Overflow Tags Network (4/7)
    14. 12. Empirical Analysis of the Stack Overflow Tags Network (5/7)
    15. 12. Empirical Analysis of the Stack Overflow Tags Network (6/7)
    16. 12. Empirical Analysis of the Stack Overflow Tags Network (7/7)
  14. Back Cover

Product information

  • Title: Text Mining and Visualization
  • Author(s): Markus Hofmann, Andrew Chisholm
  • Release date: January 2016
  • Publisher(s): Chapman and Hall/CRC
  • ISBN: 9781482237580