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.

RapidMiner RapidMiner for Text Analytic Fundamentals John RyanEmpirical Zipf-Mandelbrot Variation for Sequential Windows within Documents Andrew Chisholm KNIME Introduction to the KNIME Text Processing Extension Kilian ThielSocial Media Analysis — Text Mining Meets Network Mining Kilian Thiel, Tobias Kötter, Rosaria Silipo, and Phil Winters Python Mining Unstructured User Reviews with Python Brian CarterSentiment Classification and Visualization of Product Review Data Alexander Piazza and Pavlina DavchevaMining Search Logs for Usage Patterns Tony Russell-Rose and Paul CloughTemporally Aware Online News Mining and Visualization with Python Kyle GoslinText Classification Using Python David Colton R Sentiment Analysis of Stock Market Behavior from Twitter Using the R Tool Nuno Oliveira, Paulo Cortez, and Nelson ArealTopic Modeling Patrick BuckleyEmpirical Analysis of the Stack Overflow Tags Network Christos Iraklis Tsatsoulis

Markus Hofmann is a lecturer at the Institute of Technology Blanchardstown, where he focuses on the areas of data mining, text mining, data exploration and visualization, and business intelligence. Dr. Hofmann has also worked as a technology expert with 20 different organizations, such as Intel. He earned a PhD from Trinity College Dublin, an MSc in computing from the Dublin Institute of Technology, and a BA in information management systems. Andrew Chisholm is a certified RapidMiner Master who created both basic and advanced RapidMiner video training content for RapidMinerResources.com. He has worked as a software developer, systems integrator, project manager, solution architect, customer-facing presales consultant, and strategic consultant. He earned an MSc in business intelligence and data mining from the Institute of Technology Blanchardstown and an MA in physics from Oxford University.