A Simple and Effective Self-Supervised Contrastive Learning Framework for Aspect Detection
This work addresses aspect detection in online reviews, which is important for businesses and researchers analyzing customer feedback, but it appears incremental as it builds upon existing deep learning-based topic models.
The paper tackles the problem of noisy aspect extraction and poor mapping in unsupervised aspect detection from online reviews by proposing a self-supervised contrastive learning framework with a smooth self-attention module and a high-resolution selective mapping method, resulting in outperformance over recent unsupervised and weakly supervised approaches on benchmark datasets.
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.