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Exploring weakly supervised latent sentiment explanations for aspect-level review analysis

In sentiment analysis, aspect-level review analysis has been an important task because it can catalogue, aggregate, or summarize various opinions according to a product's properties. In this paper, we explore a new concept for aspect-level review analysis, latent sentiment explanations, which are defined as a set of informative aspect-specific sentences whose polarities are consistent with that of the review. In other words, sentiment explanations best represent a review in terms of both aspect and polarity. We formulate the problem as a structure learning problem, and sentiment explanations are modeled with latent variables. Training samples are automatically identified through a set of pre-defined aspect signature terms (i.e., without manual annotation on samples), which we term the way weakly supervised. Our major contributions lie in two folds: first, we formalize the use of aspect signature terms as weak supervision in a structural learning framework, which remarkably promotes aspect-level analysis; second, the performance of aspect analysis and document-level sentiment classification are mutually enhanced through joint modeling. The proposed method is evaluated on restaurant and hotel reviews respectively, and experimental results demonstrate promising performance in both document-level and aspect-level sentiment analysis.