Sparse Attention-Based Residual Joint Network for Aspect-Category-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) aims at identifying the sentiment polarity for a particular aspect in a review. ABSA studies based on deep learning models have exploited the attention mechanism to detect aspect-related parts. Conventional softmax-based attention mechanisms generate dense distributions, which may limit performance in tasks that inherently require sparsity. Recent studies on sparse attention transformation functions have demonstrated their effectiveness over the conventional softmax function. However, these studies primarily focus on highly sparse tasks based on self-attention architectures, leaving their applicability to the ABSA domain unexplored. In addition, most ABSA research has focused on leveraging aspect terms despite the usefulness of aspect categories. To address these issues, we propose a sparse-attention-based residual joint network (SPA-RJ Net) for the aspect-category-based sentiment analysis (ACSA) task. SPA-RJ Net incorporates two aspect-guided sparse attentions—sparse aspect-category attention and sparse aspect-sentiment attention—that introduce sparsity in attention via a sparse distribution transformation function, enabling the model to selectively focus on aspect-related information. In addition, it employs a residual joint learning framework that connects the aspect category detection (ACD) task module and the ACSA task module via residual connections, enabling the ACSA module to receive explicit guidance on relevant aspect categories from the ACD module. Our experiment validates that SPA-RJ Net consistently outperforms existing models, demonstrating the effectiveness of sparse attention and residual joint learning for aspect category-based sentiment classification.