2011/03/01 by Stefan Theußl, Theußl, Stefan, Ingo Feinerer +3
Computer Science · #Advanced Text Analysis Techniques #Big data #Computer science #Data Mining Algorithms and Applications #Data mining #Face (sociological concept) #Information retrieval #Open source #Operating system #Plug-in #Programming language #Software #Workload
paper · doi:10.57938/204f4755-8ce4-48ea-b501-b78d4390b2d9
openalex publication_date 2011/03/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
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 analyzing large corpora: (1) the amount of data to be processed in a single machine is usually limited by the available main memory (i.e., RAM), and (2) an increase of the amount of data to be analyzed leads to increasing computational workload. Fortunately, adequate parallel programming models like MapReduce and the corresponding open source implementation called Hadoop allow for processing data sets beyond what would fit into memory. In this paper we present the package "tm.plugin.dc" offering a seamless integration between "tm" and Hadoop. We show on the basis of an application in culturomics that we can efficiently handle data sets of significant size.