Text mining and visualization : case studies using open-source tools / Ed. by Markus Hofmann and Andrew Chisholm.

By:

Hofmann, Markus, Ed

Contributor(s):

Series: Chapman & Hall/CRC data mining and knowledge discovery seriesPublication details: Boca Raton: CRC Press, 2016.Description: xl, 297 pISBN:

9781482237573

Other title:

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

Subject(s):

Data mining

DDC classification:

006.35 Q6

Online resources:

Click here to access online

Contents:

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.

Summary: "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.

List(s) this item appears in: New Additions May-June 2019

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Holdings
Item type	Current library	Call number	Status	Date due	Barcode	Item holds
Books	Mahatma Gandhi University Library General Stacks	006.35 Q6 (Browse shelf(Opens below))	Available		59682

Total holds: 0

"A Champman & Hall Book."

Includes bibliographical references and index.

"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.

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