Q3-D3-LSA

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: SFB 649 Discussion Paper ; No. 2016-049

Classification and Topics

• Classification: Wirtschaft
• Subject: QuantNet ; GitHub API ; text mining ; document clustering ; similarity ; semantic web ; generalized vector space model ; LSA ; visualization

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Borke, Lukas ; Härdle, Wolfgang Karl
• Event: Veröffentlichung
• (who): Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
• (where): Berlin
• (when): 2016

Further information

• Handle: http://hdl.handle.net/10419/148886
• Last update: 10.03.2025, 11:41 AM CET