In natural language understanding, there is a hierarchy of lenses through which we can extract meaning - from words to sentences to paragraphs to documents. At the document level, one of the most useful ways to understand text is by analyzing its topics.
Available with notes: http://de.slideshare.net/ChristopherMoody3/word2vec-lda-and-introducing-a-new-hybrid-algorithm-lda2vec (Data Day 2016) Standard natural …
Labeled LDA (D. Ramage, D. Hall, R. Nallapati and C.D. Manning; EMNLP2009) is a supervised topic model derived from LDA (Blei+ 2003). While LDA's estimated topics don't often equal to human's expectation because it is unsupervised, Labeled LDA is to treat documents with multiple labels. I implemented Labeled LDA in python.
Stan modeling language and C++ library for Bayesian inference. NUTS adaptive HMC (MCMC) sampling, automatic differentiation, R, shell interfaces. Gelman.
A. Bakalov, A. McCallum, H. Wallach, and D. Mimno. Proceedings of the 12th ACM/IEEE-CS Joint Conference on Digital Libraries, page 237--240. New York, NY, USA, ACM, (2012)