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 higher semantic cohesion.

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Zhe Zhang and Munindar P. Singh. 2019. Leveraging Structural and Semantic Correspondence for Attribute-Oriented Aspect Sentiment Discovery. In Proceedings of the 24^th Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1‐10, Hong Kong. [pdf] [bib]

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