  Unsupervised aspect detection (UAD) aims at automatically extracting
interpretable aspects and identifying aspect-specific segments (such as
sentences) from online reviews. However, recent deep learning-based topic
models, specifically aspect-based autoencoder, suffer from several problems,
such as extracting noisy aspects and poorly mapping aspects discovered by
models to the aspects of interest. To tackle these challenges, in this paper,
we first propose a self-supervised contrastive learning framework and an
attention-based model equipped with a novel smooth self-attention (SSA) module
for the UAD task in order to learn better representations for aspects and
review segments. Secondly, we introduce a high-resolution selective mapping
(HRSMap) method to efficiently assign aspects discovered by the model to
aspects of interest. We also propose using a knowledge distilling technique to
further improve the aspect detection performance. Our methods outperform
several recent unsupervised and weakly supervised approaches on publicly
available benchmark user review datasets. Aspect interpretation results show
that extracted aspects are meaningful, have good coverage, and can be easily
mapped to aspects of interest. Ablation studies and attention weight
visualization also demonstrate the effectiveness of SSA and the knowledge
distilling method.
