Supreme: Make it easier applying LDA topic models to a corpus of...

In paolofantini/Supreme: Make it easier applying LDA topic models to a corpus of Italian Supreme Court decisions

Description

This package provides tools that make it easier building a corpus of documents starting from the original xml files. It also provides a set of functions for reducing the dimensionality (number of columns) of obtained document-term matrix in both cases of supervised and unsupervised matrix and implements a new strategy for selecting the number of topics based on logistic classification. This strategy can be considered as an alternative to the general criterion of perplexity.

References

David M. Blei, Andrew Y. Ng, Michael I. Jordan. Latent Dirichlet Allocation. Journal of Machine Learning Research 3 (2003) 993-1022. http://machinelearning.wustl.edu/mlpapers/paper_files/BleiNJ03.pdf.

paolofantini/Supreme documentation built on May 24, 2019, 6:14 p.m.