. Three text mining models will be examined: vector space model (VSM), generalized VSM (GVSM) and latent semantic analysis … 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 …

time within a corpus of 576 selected policy-making documents. To this end, the paper uses a combination of text mining …

supported these topics through the conduct of APEC projects. The application of text mining algorithms, such as topic modeling … Meeting (AMM), and Senior Officials' Meeting (SOM), generated themes from the text which insight have been discussed. The …

Popular approaches to building data from unstructured text come with limitations, such as scalability, interpretability … manner. CRAML produces document-level datasets for quantitative research and makes qualitative classification schemes … scalable over large volumes of text. We demonstrate that the method is useful for bibliographic analysis, transparent analysis …

We introduce unFEAR, Unsupervised Feature Extraction Clustering, to identify economic crisis regimes. Given labeled …