Complementary Aspect-Based Opinion Mining Across Asymmetric Collections

Aspect-based opinion mining is to find elaborate opinions towards an underlying theme, perspective or viewpoint as to a subject such as a product or an event. Nowadays, with rapid growing of opinionated text on the Web, mining aspect-level opinions has become a promising means for online public opinion analysis. In particular, the booming of various types of online media provide diverse yet complementary information, bringing unprecedented opportunities for public opinion analysis across different populations. Along this line, in this paper, we propose CAMEL, a novel topic model for complementary aspect-based opinion mining across asymmetric collections. CAMEL gains complementarity by modeling both common and specific aspects across different collections, and keeping all the corresponding opinions for contrastive study. To further boost CAMEL, we propose AME, an automatic labeling scheme for maximum entropy model, to help discriminate aspect and opinion words without heavy human labeling. Extensive experiments on synthetic multicollection data sets demonstrate the superiority of CAMEL to baseline methods, in leveraging cross-collection complementarity to find higher-quality aspects and more coherent opinions as well as aspect-opinion relationships. This is particularly true when the collections get seriously imbalanced. Experimental results also show that the AME model indeed outperforms manual labeling in suggesting true opinion words. Finally, case study on two public events further demonstrates the practical value of CAMEL for real-world public opinion analysis.