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| * 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. | | * 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. | | ** 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> |
Latest revision as of 12:23, 25 September 2014
Tm - Text mining package 0.5-10 (2014/01/13)
Developed by: Ingo Feinerer
License: GPL / GNU General Public License
Web page : Tool homepage
Tool type : Plugin of R
The last edition of this page was on: 2014/03/24
The Completion level of this page is : Medium
The last edition of this page was on: 2014/03/24 The Completion level of this page is : Medium
SHORT 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.
TOOL CHARACTERISTICS
Usability
Authors of this page consider that this tool is '.
Tool orientation
This tool is designed for general purpose analysis.
Data mining type
This tool is made for Structured data mining, Text mining.
Manipulation type
This tool is designed for Data extraction, Data transformation, Data analysis, Data visualisation.
IMPORT FORMAT :
PDF, TXT, XML
| Tool objective(s) in the field of Learning Sciences |
|
☑ Analysis & Visualisation of data
☑ Predicting student performance
☑ Student modelling
☑ Social Network Analysis (SNA)
☑ Constructing courseware
|
☑ Providing feedback for supporting instructors:
☑ Recommendations for students
☑ Grouping students:
☑ Developing concept maps:
☑ Planning/scheduling/monitoring
☑ Experimentation/observation
|
Tool can perform:
- Data extraction of type:
- Transformation of type:
- Data analysis of type: Basic statistics and data summarization, Data mining methods and algorithms
- Data visualisation of type: Sequential Graphic, Chart/Diagram, Map (These visualisations can be interactive and updated in "real time")
ABOUT USERS
Tool is suitable for:
Students/Learners/Consumers
Teachers/Tutors/Managers
Researchers
Developers/Designers
Organisations/Institutions/Firms
Others
Required skills:
SYSTEM ADMINISTRATION: None
DATA MINING MODELS: Advanced
FREE TEXT
Tool version : Tm - Text mining package 0.5-10 2014/01/13
(blank line)
Developed by : Ingo Feinerer
(blank line)
Tool Web page : http://tm.r-forge.r-project.org/
(blank line)
Tool type : Plugin of R
(blank line)
License:GPL / GNU General Public License
| 
|
SHORT 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.
TOOL CHARACTERISTICS
| Tool orientation |
Data mining type |
Usability |
| This tool is designed for general purpose analysis. |
This tool is designed for Structured data mining, Text mining. |
Authors of this page consider that this tool is . |
| Data import format |
Data export format |
| PDF, TXT, XML. |
. |
| Tool objective(s) in the field of Learning Sciences |
|
☑ Analysis & Visualisation of data
☑ Predicting student performance
☑ Student modelling
☑ Social Network Analysis (SNA)
☑ Constructing courseware
|
☑ Providing feedback for supporting instructors:
☑ Recommendations for students
☑ Grouping students:
☑ Developing concept maps:
☑ Planning/scheduling/monitoring
☑ Experimentation/observation
|
Can perform data extraction of type:
Can perform data transformation of type:
Can perform data analysis of type:
Basic statistics and data summarization, Data mining methods and algorithms
Can perform data visualisation of type:
Sequential Graphic, Chart/Diagram, Map (These visualisations can be interactive and updated in "real time")
ABOUT USER
| Tool is suitable for: |
| Students/Learners/Consumers:☑ |
Teachers/Tutors/Managers:☑ |
Researchers:☑ |
Organisations/Institutions/Firms:☑ |
Others:☑ |
| Required skills: |
| Statistics: ADVANCED |
Programming: BASIC |
System administration: NONE |
Data mining models: ADVANCED |
OTHER TOOL INFORMATION
|
| Tm.png
|
| Rlogo.jpg
|
| Tm - Text mining package
|
| GPL / GNU General Public License
|
| Free&Open source
|
| Ingo Feinerer
|
| 2014/01/13
|
| 0.5-10
|
| http://tm.r-forge.r-project.org/
|
| 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.
|
| General analysis
|
|
|
| Teachers/Tutors/Managers, Researchers
|
| Advanced
|
| Basic
|
| None
|
| Advanced
|
| Plugin/extension pack
|
|
|
|
|
| Structured data mining, Text mining
|
| Data extraction, Data transformation, Data analysis, Data visualisation
|
| Basic statistics and data summarization, Data mining methods and algorithms
|
|
|
| PDF, TXT, XML
|
|
|
|
|
|
|
| Sequential Graphic, Chart/Diagram, Map
|
|
|
|
|
| R
|
| Medium
|
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
??tm
