Unlike vocabulary selection techniques that separate domain-independent words from topicspecific keywords using word collocation statistics, HMM-LDA classifies each word instance according to its context.
the algorithm creates a Markov chain whose stationary distribution matches the expected distribution of the state and topic labels for each word in the training corpus.