LDA for sentiment analysis As far as I understand it, LDA works by assuming that a corpus was written by a set of topics ands words corresponding to that topic by a specific distribution. I'm however not enterely sure what the output of the LDA process is and more importantly, how one can (for example) model a corpus of political speeches to their sentiments.
LDA is a unsupervised technique which identifies the set of words that compromise of a topic.
This is done by assuming that there is a generator that generates the sentences using the words.
To simplify the assumptions on the number of topics are made which ideally has to be learnt from the data.

The output of LDA is the words that represent a topic and probability of a sentence/ text observation being a mixture of the N topics we initially assumed.

We will get a 1xN row vector for each text observation that has probability of it containing topic n (which is one of the N topics)