Abstract
In this paper we proposed a hierarchical generative model based on Naïve 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 Naïve 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.
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Zhang, Y., Ji, DH., Su, Y., Wu, H. (2013). Joint Naïve Bayes and LDA for Unsupervised Sentiment Analysis. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7818. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37453-1_33
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DOI: https://doi.org/10.1007/978-3-642-37453-1_33
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