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Opinionated text often involves attributes such as authorship and locationthat influence the sentiments expressed for different aspects. We posit thatstructural and semantic correspondence is both prevalent in opinionated text,especially when associated with attributes, and crucial in accurately revealingits latent aspect and sentiment structure. However, it is not recognized byexisting approaches.  We propose Trait, an unsupervised probabilistic model that discovers aspectsand sentiments from text and associates them with different attributes. To thisend, Trait infers and leverages structural and semantic correspondence using aMarkov Random Field. We show empirically that by incorporating attributesexplicitly Trait significantly outperforms state-of-the-art baselines both bygenerating attribute profiles that accord with our intuitions, as shown viavisualization, and yielding topics of greater semantic cohesion.

Leveraging Structural and Semantic Correspondence for Attribute-Oriented Aspect Sentiment Discovery