Abstract
Aspect extraction and sentiment analysis of reviews are both important tasks in opinion mining. We propose a novel sentiment and aspect extraction model based on Restricted Boltzmann Machines to jointly address these two tasks in an unsupervised setting. This model reflects the generation process of reviews by introducing a heterogeneous structure into the hidden layer and incorporating informative priors. Experiments show that our model outperforms previous state-of-The-Art methods.
Cite
CITATION STYLE
Wang, L., Liu, K., Cao, Z., Zhao, J., & De Melo, G. (2015). Sentiment-Aspect extraction based on restricted Boltzmann machines. In ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference (Vol. 1, pp. 616–625). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/p15-1060
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