Sentiment Extraction by Leveraging Aspect-Opinion Association Structure - Zhao, Li; Huang, Minlie; Sun, Jiashen; Luo, Hengliang; Yang, Xiankai; Zhu, Xiaoyan (Proceedings of the 24th ACM International Conference on Information and Knowledge Management, 2015)
Sentiment extraction aims to extract and group aspect and opinion words from online reviews. Previous works usually extract aspect and opinion words by leveraging association between a single pair of aspect and opinion word, but the structure of aspect and opinion word clusters has not been fully exploited. In this paper, we investigate the aspect-opinion association structure, and propose a “first clustering, then extracting” unsupervised model to leverage properties of the structure for sentiment extraction. For the clustering purpose, we formalise a novel concept syntactic distribution consistency as soft constraint in the framework of posterior regularization; for the extraction purpose, we extract aspect and opinion words based on cluster-cluster association. In comparison to traditional word-word association, we show that cluster-cluster association is a much stronger signal to distinguish aspect (opinion) words from non-aspect (non-opinion) words. Extensive experiments demonstrate the effectiveness of the proposed approach and the advantages against stateof-the-art baselines.