Text Mining and Visualization: Case Studies Using Open-Source Tools

Text Mining and Visualization: Case Studies Using Open-Source Tools 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―all highly experienced with text mining and open-source software―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 you can follow as part of a step-by-step, reproducible example. You can also easily apply and extend the techniques to other problems. All the examples are available on a supplementary website.

The book shows you how to exploit your text data, offering successful application examples and blueprints for you to tackle your text mining tasks and benefit from open and freely available tools. It gets you up to date on the latest and most powerful tools, the data mining process, and specific text mining activities.

Table of Contents

Part I: RapidMiner
Chapter 1. RapidMiner for Text Analytic Fundamentals
Chapter 2. Empirical Zipf-Mandelbrot Variation for Sequential Windows within Documents

Part II: KNIME
Chapter 3. Introduction to the KNIME Text Processing Extension
Chapter 4. Social Media Analysis – Text Mining Meets Network Mining

Part III: Python
Chapter 5. Mining Unstructured User Reviews with Python
Chapter 6. Sentiment Classification and Visualization of Product Review Data
Chapter 7. Mining Search Logs for Usage Patterns
Chapter 8. Temporally Aware Online News Mining and Visualization with Python
Chapter 9. Text Classification Using Python

Part IV: R
Chapter 10. Sentiment Analysis of Stock Market Behavior from Twitter Using the R Tool
Chapter 11. Topic Modeling
Chapter 12. Empirical Analysis of the Stack Overflow Tags Network