We will view sentiment analysis as a regression problem: given an input document, we want to predict a real-valued observation y that represents the sentiment of a document.
we build on supervised latent Dirichlet allocation (SLDA, (Blei and McAuliffe, 2007)), which makes predictions based on the topics expressed in a document; this can be thought of projecting the words in a document to low dimensional space of dimension equal to the number of topics.