Text Mining Infrastructure in<i>R</i>

Инфраструктура интеллектуального анализа текстов в R
Ingo Feinerer, Kurt Hornik, David Meyer
2008-01-01

R packagecount-based analysistext classificationtext clusteringtext mining
During the last decade text mining has become a widely used discipline utilizing statistical and machine learning methods. We present the <strong>tm</strong> 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.
1
The framework supports count-based text analysis, text clustering, text classification, and string-kernel methods.
2
The package is designed to support statistical and machine-learning approaches to text mining within R.
3
The paper surveys text-mining facilities available in R and demonstrates how typical application tasks can be performed.
4
The tm package provides an R framework for developing and applying text-mining applications.

tm package framework for text mining applications within R

Text mining facilities and application tasks, including count-based analysis, text clustering, text classification, and string kernels

Publication Details
Publication Date
2008-01-01
Journal
Publisher
ISSN
Access Type
Author Information
Authors
Ingo Feinerer
Kurt Hornik
David Meyer
Explore further
Open the scid.ai AI chat with a ready-made request: it will find papers on a similar topic and help build a literature review.
Find similar papers in the chat
Make a presentation
100%