  Aspect-based sentiment analysis (ABSA) is a widely studied topic, most often
trained through supervision from human annotations of opinionated texts. These
fine-grained annotations include identifying aspects towards which a user
expresses their sentiment, and their associated polarities (aspect-based
sentiments). Such fine-grained annotations can be expensive and often
infeasible to obtain in real-world settings. There is, however, an abundance of
scenarios where user-generated text contains an overall sentiment, such as a
rating of 1-5 in user reviews or user-generated feedback, which may be
leveraged for this task. In this paper, we propose a VAE-based topic modeling
approach that performs ABSA using document-level supervision and without
requiring fine-grained labels for either aspects or sentiments. Our approach
allows for the detection of multiple aspects in a document, thereby allowing
for the possibility of reasoning about how sentiment expressed through multiple
aspects comes together to form an observable overall document-level sentiment.
We demonstrate results on two benchmark datasets from two different domains,
significantly outperforming a state-of-the-art baseline.
