Comparative Relation Generative Model

Online reviews are important decision aids to consumers. Other than helping users to evaluate individual products, reviews also support comparison shopping by comparing two (or more) products based on a specific aspect. However, making a comparison across two different reviews, written by different authors, is not always equitable due to the different standards and preferences of authors. Therefore, we focus on comparative sentences, whereby two products are compared directly by a review author within a sentence. We study the problem of comparative relation mining. Given a set of comparative sentences, each relating a pair of entities, our objective is three-fold: to interpret the comparative direction in each sentence, to identify the aspect of each sentence, and to determine the relative merits of each entity with respect to that aspect. This requires mining comparative relations at two levels of resolution: at the sentence level, and at the entity level. Our insight is that there is a significant synergy between the two levels. We propose a generative model for comparative text, which jointly models comparative directions at the sentence level, and ranking at the entity level. This model is tested comprehensively on Amazon reviews dataset with good empirical outperformance over pipelined baselines.