  Opinionated text often involves attributes such as authorship and location
that influence the sentiments expressed for different aspects. We posit that
structural and semantic correspondence is both prevalent in opinionated text,
especially when associated with attributes, and crucial in accurately revealing
its latent aspect and sentiment structure. However, it is not recognized by
existing approaches.
  We propose Trait, an unsupervised probabilistic model that discovers aspects
and sentiments from text and associates them with different attributes. To this
end, Trait infers and leverages structural and semantic correspondence using a
Markov Random Field. We show empirically that by incorporating attributes
explicitly Trait significantly outperforms state-of-the-art baselines both by
generating attribute profiles that accord with our intuitions, as shown via
visualization, and yielding topics of greater semantic cohesion.
