  Sentiment analysis is an important task in natural language processing (NLP).
Most of existing state-of-the-art methods are under the supervised learning
paradigm. However, human annotations can be scarce. Thus, we should leverage
more weak supervision for sentiment analysis. In this paper, we propose a
posterior regularization framework for the variational approach to the weakly
supervised sentiment analysis to better control the posterior distribution of
the label assignment. The intuition behind the posterior regularization is that
if extracted opinion words from two documents are semantically similar, the
posterior distributions of two documents should be similar. Our experimental
results show that the posterior regularization can improve the original
variational approach to the weakly supervised sentiment analysis and the
performance is more stable with smaller prediction variance.
