Clustering Aspect-related Phrases by Leveraging Sentiment Distribution Consistency
Zhao Li, Minlie Huang, Haiqiang Chen, Junjun Cheng, Xiaoyan Zhu · 2014
Clustering aspect-related phrases in terms of product’s property is a precursor pro-cess to aspect-level sentiment analysis which is a central task in sentiment analy-sis. Most of existing methods for address-ing this problem are context-based models which assume that domain synonymous phrases share similar co-occurrence con-texts. In this paper, we explore a novel idea, sentiment distribution consistency, which states that different phrases (e.g. “price”, “money”, “worth”, and “cost”) of the same aspect tend to have consistent sentiment distribution. Through formal-izing sentiment distribution consistency as soft constraint, we propose a novel unsu-pervised model in the framework of Poste-rior Regularization (PR) to cluster aspect-related phrases. Experiments demonstrate that our approach outperforms baselines remarkably. 1