Abstract
Clustering sentiment phrases in product reviews is convenient for us to get the most important information about one product directly through thousands of reviews. There are mainly two components in a sentiment phrase, the aspect word and the opinion word. We need to cluster these two parts simultaneously. Although several methods have been proposed to cluster words or phrases, limited work has been done on clustering two-dimensional sentiment phrases. In this paper, we apply a two-sided hidden Markov random field (HMRF) model on this task. We use the approach of constrained co-clustering with some priori knowledge, in a semi-supervised setting. Experimental results on sentiment phrases extracted from about 0.7 million mobile phone reviews show that this method is promising for this task and our method outperforms baselines remarkably.
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© 2015 Springer International Publishing Switzerland
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Cao, Y., Huang, M., Zhu, X. (2015). Clustering Sentiment Phrases in Product Reviews by Constrained Co-clustering. In: Li, J., Ji, H., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2015. Lecture Notes in Computer Science(), vol 9362. Springer, Cham. https://doi.org/10.1007/978-3-319-25207-0_7
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DOI: https://doi.org/10.1007/978-3-319-25207-0_7
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