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{{Data mining and learning analytics tools | {{Data mining and learning analytics tools | ||
|field_logo= | |field_logo=Rlogo.jpg | ||
|field_screenshot=Tm.png | |field_screenshot=Tm.png | ||
|field_name=Tm - Text mining package | |field_name=Tm - Text mining package | ||
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|field_data_tool_type=Plugin/extension pack | |field_data_tool_type=Plugin/extension pack | ||
|field_plugin_of=R | |field_plugin_of=R | ||
|field_language= | |||
|field_license_type=Free&Open source | |field_license_type=Free&Open source | ||
|field_free_software_licence=GPL / GNU General Public License | |field_free_software_licence=GPL / GNU General Public License | ||
|field_last_release=2014/01/13 | |field_last_release=2014/01/13 | ||
|field_last_version= | |field_last_version=0.5-10 | ||
|field_description=tm package provides a framework for text mining applications within R. The tm package offers functionality for managing text documents, abstracts the process of document manipulation and eases the usage of heterogeneous text formats in R. The package provides native support for reading in several classic file formats such as plain text, PDFs, or XML files. There is also a plug-in mechanism to handle additional file formats. The data structures and algorithms can be extended to fit custom demands. | |field_description=tm package provides a framework for text mining applications within R. The tm package offers functionality for managing text documents, abstracts the process of document manipulation and eases the usage of heterogeneous text formats in R. The package provides native support for reading in several classic file formats such as plain text, PDFs, or XML files. There is also a plug-in mechanism to handle additional file formats. The data structures and algorithms can be extended to fit custom demands. | ||
|field_analysis_orientation=General analysis | |field_analysis_orientation=General analysis | ||
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|field_import_format= | |field_import_format= | ||
|field_export_format= | |field_export_format= | ||
|field_analysis_type=Data mining methods and algorithms | |field_analysis_type=Basic statistics and data summarization, Data mining methods and algorithms | ||
|field_visualisation_type=Sequential Graphic, Chart/Diagram, Map | |field_visualisation_type=Sequential Graphic, Chart/Diagram, Map | ||
|field_tool_usability= | |field_tool_usability= | ||
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|field_last_edition=2014/03/24 | |field_last_edition=2014/03/24 | ||
}} | }} | ||
'''Publications''' | |||
* Ingo Feinerer, Kurt Hornik, David Meyer (2008). Text Mining Infrastructure in R, Journal of Statistics Sofware, Vol. 25, Issue 5, Mar 2008. http://www.jstatsoft.org/v25/i05. | |||
** Abstract: During the last decade text mining has become a widely used discipline utilizing statistical and machine learning methods. We present the tm package which provides a framework for text mining applications within R. We give a survey on text mining facilities in R and explain how typical application tasks can be carried out using our framework. We present techniques for count-based analysis methods, text clustering, text classification and string kernels. | |||
'''Documentation''' | |||
* Try the built-in documentation first, e.g. type <code>??tm</code> | |||
