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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...

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Detalles Bibliográficos
Autores principales: Hofmann, Markus, Chisholm, Andrew
Lenguaje:eng
Publicado: CRC Press 2016
Materias:
Acceso en línea:http://cds.cern.ch/record/2120828
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author Hofmann, Markus
Chisholm, Andrew
author_facet Hofmann, Markus
Chisholm, Andrew
author_sort Hofmann, Markus
collection CERN
description 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.
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spelling cern-21208282021-04-21T19:55:33Zhttp://cds.cern.ch/record/2120828engHofmann, MarkusChisholm, AndrewText mining and visualization: case studies using open-source toolsComputing and ComputersText 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.CRC Pressoai:cds.cern.ch:21208282016
spellingShingle Computing and Computers
Hofmann, Markus
Chisholm, Andrew
Text mining and visualization: case studies using open-source tools
title Text mining and visualization: case studies using open-source tools
title_full Text mining and visualization: case studies using open-source tools
title_fullStr Text mining and visualization: case studies using open-source tools
title_full_unstemmed Text mining and visualization: case studies using open-source tools
title_short Text mining and visualization: case studies using open-source tools
title_sort text mining and visualization: case studies using open-source tools
topic Computing and Computers
url http://cds.cern.ch/record/2120828
work_keys_str_mv AT hofmannmarkus textminingandvisualizationcasestudiesusingopensourcetools
AT chisholmandrew textminingandvisualizationcasestudiesusingopensourcetools