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pro vyhledávání: '"Feinerer, Ingo"'
R has recently gained explicit text mining support with the "tm" package enabling statisticians to answer many interesting research questions via statistical analysis or modeling of (text) corpora. However, we typically face two challenges when analy
Social network analysis (SNA) provides tools to examine relationships between people. Text mining (TM) allows capturing the text they produce in Web 2.0 applications, for example, however it neglects their social structure. This paper applies an appr
Externí odkaz:
http://epub.wu.ac.at/1654/1/document.pdf
Autor:
Bohn, Angela, Theußl, Stefan, Feinerer, Ingo, Hornik, Kurt, Mair, Patrick, Walchhofer, Norbert
In Social Network Analysis (SNA) centrality measures focus on activity (degree), information access (betweenness), distance to all the nodes (closeness), or popularity (pagerank). We introduce a new measure quantifying the distance of nodes to the ne
Externí odkaz:
http://epub.wu.ac.at/1466/1/document.pdf
Autor:
Feinerer, Ingo, Hornik, Kurt
Within the last decade text mining, i.e., extracting sensitive information from text corpora, has become a major factor in business intelligence. The automated textual analysis of law corpora is highly valuable because of its impact on a company's le
Externí odkaz:
http://epub.wu.ac.at/152/1/document.pdf
We present a package which provides a general framework, including tools and algorithms, for text mining in R using the S4 class system. Using this package and the kernlab R package we explore the use of kernel methods for clustering (e.g., kernel k-
Externí odkaz:
http://epub.wu.ac.at/1002/1/document.pdf
Autor:
Feinerer, Ingo, Buchta, Christian, Geiger, Wilhelm, Rauch, Johannes, Mair, Patrick, Hornik, Kurt
Identifying the language used will typically be the first step in most natural language processing tasks. Among the wide variety of language identification methods discussed in the literature, the ones employing the Cavnar and Trenkle (1994) approach
Externí odkaz:
http://epub.wu.ac.at/3985/1/textcat.pdf
R has gained explicit text mining support with the tm package enabling statisticians to answer many interesting research questions via statistical analysis or modeling of (text) corpora. However, we typically face two challenges when analyzing large
Externí odkaz:
http://epub.wu.ac.at/3974/1/plugin.pdf
http://epub.wu.ac.at/3974/2/tm.plugin.dc_0.2%2D4.tar.gz
http://epub.wu.ac.at/3974/2/tm.plugin.dc_0.2%2D4.tar.gz
Clustering text documents is a fundamental task in modern data analysis, requiring approaches which perform well both in terms of solution quality and computational efficiency. Spherical k-means clustering is one approach to address both issues, empl
Externí odkaz:
http://epub.wu.ac.at/4000/1/paper.pdf
Social Network Analysis (SNA) provides tools to examine relationships between people. Text Mining (TM) allows capturing the text they produce inWeb 2.0 applications, for example, however it neglects their social structure. This paper applies an appro
Externí odkaz:
http://epub.wu.ac.at/5435/1/RJournal_2011%2D1_Bohn~et~al.pdf
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 fac
Externí odkaz:
http://epub.wu.ac.at/3978/1/textmining.pdf