Text Mining and Visualization: Case Studies Using Open-Source Tools

This book 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 explain how text data are gathered and processed from a wide variety of sources, including books, server access logs, websites, so...

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Bibliographische Detailangaben
Hauptverfasser:	Hofmann, Markus, Chisholm, Andrew
Format:	Buch
Sprache:	eng
Schlagworte:	analysis COMPUTERSCIENCEnetBASE Data mining Data Preparation & Mining document Economics expression INFORMATIONSCIENCEnetBASE machine Machine Learning Natural language processing (Computer science) regular SCI-TECHnetBASE sentiment Statistical Computing STATSnetBASE STMnetBASE support term vector
Online-Zugang:	Volltext
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Beschreibung
Zusammenfassung:	This book 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 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 readers can follow as part of a step-by-step, reproducible example. The examples used are available on a supplementary website.
DOI:	10.1201/b19007