  In this paper, we propose a variational approach to unsupervised sentiment
analysis. Instead of using ground truth provided by domain experts, we use
target-opinion word pairs as a supervision signal. For example, in a document
snippet "the room is big," (room, big) is a target-opinion word pair. These
word pairs can be extracted by using dependency parsers and simple rules. Our
objective function is to predict an opinion word given a target word while our
ultimate goal is to learn a sentiment classifier. By introducing a latent
variable, i.e., the sentiment polarity, to the objective function, we can
inject the sentiment classifier to the objective function via the evidence
lower bound. We can learn a sentiment classifier by optimizing the lower bound.
We also impose sophisticated constraints on opinion words as regularization
which encourages that if two documents have similar (dissimilar) opinion words,
the sentiment classifiers should produce similar (different) probability
distribution. We apply our method to sentiment analysis on customer reviews and
clinical narratives. The experiment results show our method can outperform
unsupervised baselines in sentiment analysis task on both domains, and our
method obtains comparable results to the supervised method with hundreds of
labels per aspect in customer reviews domain, and obtains comparable results to
supervised methods in clinical narratives domain.
