Q3-D3-LSA

Lukas Borke and Wolfgang Härdle

No 2016-049, SFB 649 Discussion Papers from Humboldt University Berlin, Collaborative Research Center 649: Economic Risk

Abstract: QuantNet 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.

Keywords: QuantNet; D3; GitHub API; text mining; document clustering; similarity; semantic web; generalized vector space model; LSA; visualization (search for similar items in EconPapers)
Date: 2016
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