LDA for sentiment analysis
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)