We summarise the generative process of contraLDA as follows: The contraLDA model can be trained flexibly depending on the type of supervision information available.
if there are only labelled features available (e.g., sentiment lexicon, or topic seed words), our model will incorporate the labelled features to constrain the Dirichlet prior of topic-word distributions, which essentially plays a role in governing the model inference.