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

Variational Weakly Supervised Sentiment Analysis with Posterior Regularization