(xl, 297 pages, 10 unnumbered pages of plates) : illustrations.
Chapman & Hall/CRC data mining and knowledge discovery series.
A Chapman & Hall book.
Front Cover; Contents; I: RapidMiner; 1. RapidMiner for Text Analytic Fundamentals; 2. Empirical Zipf-Mandelbrot Variation for Sequential Windows within Documents; II: KNIME; 3. Introduction to the KNIME Text Processing Extension; 4. Social Media Analysis --; Text Mining Meets Network Mining; III: Python; 5. Mining Unstructured User Reviews with Python; 6. Sentiment Classification and Visualization of Product Review Data; 7. Mining Search Logs for Usage Patterns; 8. Temporally Aware Online News Mining and Visualization with Python; 9. Text Classification Using Python; IV: R. 10. Sentiment Analysis of Stock Market Behavior from Twitter Using the R Tool11. Topic Modeling; 12. Empirical Analysis of the Stack Overflow Tags Network; Back Cover.
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--Back cover.