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Text mining and visualization : = ca...

Hofmann, Markus.

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

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Text mining and visualization :/ edited by Markus Hofmann, Andrew Chisholm.
Reminder of title:	case studies using open-source tools /
other author:	Hofmann, Markus.
Published:	Boca Raton :CRC Press, : c2016.,
Description:	xl, 297 p., [10] p. of plates :ill. ;26 cm.
Notes:	"A Champman & Hall Book."
[NT 15003449]:	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.
Subject:	Data mining. -
ISBN:	9781482237573

Text mining and visualization : = case studies using open-source tools /
Text mining and visualization :case studies using open-source tools /edited by Markus Hofmann, Andrew Chisholm. - Boca Raton :CRC Press,c2016. - xl, 297 p., [10] p. of plates :ill. ;26 cm. - Chapman & Hall/CRC data mining and knowledge discovery series. - Chapman & Hall/CRC data mining and knowledge discovery series..

"A Champman & Hall Book."

Includes bibliographical references and index.

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.

"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.

ISBN: 9781482237573UK63.99

LCCN: 2016301980Subjects--Topical Terms:

562972
Data mining.

LC Class. No.: QA76.9.D343 / T49 2016

Dewey Class. No.: 006.3/12

Text mining and visualization : = case studies using open-source tools /
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020 $a 9781482237573 $q (hbk.) : $c UK63.99
020 $a 1482237571 $q (hbk.)
020 $z 9781482237580 $q (PDF ebook)
035 $a (OCoLC)ocn911801405
040 $a YDXCP $b eng $c YDXCP $d BTCTA $d BDX $d KSU $d OCLCQ $d OCLCF $d NUI $d CDX $d CHVBK $d SOI $d OCLCQ $d DLC
042 $a lccopycat
050 0 0 $a QA76.9.D343 $b T49 2016
082 0 4 $a 006.3/12 $2 23
245 0 0 $a Text mining and visualization : $b case studies using open-source tools / $c edited by Markus Hofmann, Andrew Chisholm.
260 $a Boca Raton : $b CRC Press, $c c2016.
300 $a xl, 297 p., [10] p. of plates : $b ill. ; $c 26 cm.
490 1 $a Chapman & Hall/CRC data mining and knowledge discovery series
500 $a "A Champman & Hall Book."
504 $a Includes bibliographical references and index.
505 0 $a 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.
520 $a "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.
650 0 $a Data mining. $3 562972
700 1 $a Hofmann, Markus. $3 842106
700 1 $a Chisholm, Andrew, $d 1959- $3 744483
830 0 $a Chapman & Hall/CRC data mining and knowledge discovery series. $3 1097255