In this paper we proposed a hierarchical generative model based on Naive Bayes and LDA for unsupervised sentiment analysis at sentence level and document level of granularity simultaneously. In particular, our model called NB-LDA assumes that each sentence instead of word has a latent sentiment label, and then the sentiment label generates a series of features for the sentence independently in the Naive Bayes manner. The idea of NB assumption at sentence level makes it possible that we can use advanced NLP technologies such as dependency parsing to improve the performance for unsupervised sentiment analysis. Experiment results show that the proposed NB-LDA can obtain significantly improved results for sentiment analysis comparing to other approaches.
['Yong Zhang', 'Donghong Ji', 'Ying Su', 'Hongmiao Wu']
Joint Naïve Bayes and LDA for Unsupervised Sentiment Analysis