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Text mining and visualization : case studies using open-source tools

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概要 "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.続きを見る
目次 RapidMiner for text analytic fundamentals
John Ryan
Empirical Zipf-Mandelbrot variation for sequential windows within documents
Andrew Chisholm
Introduction to the KNIME text processing extention
Kilian Thiel
Social media analysis
text mining meets network mining
Kilian Thiel, Tobias Kötter, Rosaria Silipo, and Phil Winters
Mining unstructured user reviews with Python
Brian Carter
Sentiment classification and visualization of product review data
Alexander Piazza and Pavlina Davcheva
Mining search logs for usage patterns
Tony Russell-Rose and Paul Clough
Temporally aware online news mining and visualization with Python
Kyle Goslin
Text classification using Python
David Colton
Sentiment analysis of stock market behavior from Twitter using the R tool
Nun Oliverira, Paulo Cortez, and Nelson Areal
Topic modeling
Patrick Buckley
Empiricial analysis of the stack overflow tags network
Christos Iraklis Tsatsoulis.
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本文を見る Taylor & Francis eBooks DRM Free Collection: 2015

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登録日 2023.01.20
更新日 2023.01.20