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2016-11-15Diskussionpapier DOI: 10.18452/18425
Q3-D3-LSA
dc.contributor.authorBorke, Lukas
dc.contributor.authorHärdle, Wolfgang K.
dc.date.accessioned2017-10-02T09:22:17Z
dc.date.available2017-10-02T09:22:17Z
dc.date.issued2016-11-15
dc.identifier.urihttp://edoc.hu-berlin.de/18452/19102
dc.description.abstractQuantNet 1 is an integrated web-based environment consisting of different types of statistics-related documents and program codes. Its goal is creating reproducibility and offering a platform for sharing validated knowledge native to the social web. To increase the information retrieval (IR) efficiency there is a need for incorporating semantic information. Three text mining models will be examined: vector space model (VSM), generalized VSM (GVSM) and latent semantic analysis (LSA). The LSA has been successfully used for IR purposes as a technique for capturing semantic relations between terms and inserting them into the similarity measure between documents. Our results show that different model configurations allow adapted similarity-based document clustering and knowledge discovery. In particular, different LSA configurations together with hierarchical clustering reveal good results under M3 evaluation. QuantNet and the corresponding Data-Driven Documents (D3) based visualization can be found and applied under http://quantlet.de. The driving technology behind it is Q3-D3-LSA, which is the combination of “GitHub API based QuantNet Mining infrastructure in R”, LSA and D3 implementation.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin
dc.subjectQuantNeteng
dc.subjectD3eng
dc.subjectGitHub APIeng
dc.subjecttext miningeng
dc.subjectdocument clusteringeng
dc.subjectsimilarityeng
dc.subjectsemantic webeng
dc.subjectgeneralized vector space modeleng
dc.subjectLSAeng
dc.subjectvisualizationeng
dc.subject.ddc330 Wirtschaft
dc.titleQ3-D3-LSA
dc.typeworkingPaper
dc.identifier.urnurn:nbn:de:kobv:11-110-18452/19102-6
dc.identifier.doihttp://dx.doi.org/10.18452/18425
local.edoc.container-titleSonderforschungsbereich 649: Ökonomisches Risiko
local.edoc.pages48
local.edoc.type-nameDiskussionpapier
local.edoc.institutionWirtschaftswissenschaftliche Fakultät
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume2016
local.edoc.container-issue49
local.edoc.container-erstkatid2195055-6

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