To tackle this problem, models in the literature usually use some seed words for each sentiment topic to define Dirichlet priors with asymmetric concentration parameter vectors (Sauper et al., 2011;Kim et al., 2013), or use seed words to initialize word assignment to sentiment topic (Lin and He, 2009), or both Jo and Oh, 2011).
these seed words are usually arbitrarily selected, and how to define asymmetric priors is not clear, especially when we would like to capture more than two (positive and negative) kinds of sentiments.