Leveraging Structural and Semantic Correspondence for Attribute-Oriented Aspect Sentiment Discovery
This addresses the problem of attribute-oriented aspect sentiment analysis for researchers and practitioners in natural language processing, representing an incremental improvement over existing unsupervised methods.
The paper tackles the problem of discovering aspects and sentiments from opinionated text while associating them with attributes like authorship and location, proposing an unsupervised probabilistic model called Trait that leverages structural and semantic correspondence using a Markov Random Field. The result shows that Trait significantly outperforms state-of-the-art baselines by generating attribute profiles that align with intuitions and yielding topics with greater semantic cohesion.
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.