This proposition therefore provides insight into how our model builds on PMI.
in contrast to PMI, 1) our model can consider lexical features other than gender, 2) our model is regularized to avoid the pitfalls of maximumlikelihood estimation, and 3) our model cleanly incorporates latent sentiments, relying on posterior regularization to ensure that the p(s | ν) is close to the sentiment lexicon of Hoyle et al.