Abstract: Automatic identification of salient aspects from user reviews is especially

useful for opinion analysis. There has been significant progress in utilizing

weakly supervised approaches, which require only a small set of seed words for

training aspect classifiers. However, there is still room for improvement,

as these methods do not employ hyperbolic space information, nor do they

account for the different latent semantics of individual seed words.

We propose HDAE, a hyperbolic disentangled aspect extractor

which includes a hyperbolic aspect classifier to infer review segments'

aspects, and an aspect-disentangled representation to model the latent semantics of

each seed word. Superior performance on two datasets and the embedding visualization

demonstrate that \system is a more effective approach to leveraging

seed words. We further show the effectiveness of the proposed components by

an ablation study and a case study.