. 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 …

) correspondence analysis, and (5) multidimensional scaling. Results: Word clouds show the most frequent words in a body of text …

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 …

clustering, and topic modeling, are employed for the investigation. The pre-processing step of text cleaning is first performed …) on 98 failed banks from 2008 to 2015. The text mining techniques via machine learning, i.e. bag of words, document … insights extracted from semi-structured textual data, i.e. the FDIC's reports. Our text analytics suggests that to prevent from …