Deep Context- and Relation-Aware Learning for Aspect-based Sentiment Analysis
This work addresses the problem of accurately analyzing sentiment for multiple aspects in text, which is important for applications like review analysis, but it appears incremental as it builds on existing unified approaches with novel modules.
The paper tackled the challenge of aspect-based sentiment analysis by proposing DCRAN, which integrates deep contextual information and explicit self-supervised strategies to improve prediction of multiple aspect-opinion pairs, achieving significant performance gains over state-of-the-art methods on three benchmarks.
Existing works for aspect-based sentiment analysis (ABSA) have adopted a unified approach, which allows the interactive relations among subtasks. However, we observe that these methods tend to predict polarities based on the literal meaning of aspect and opinion terms and mainly consider relations implicitly among subtasks at the word level. In addition, identifying multiple aspect-opinion pairs with their polarities is much more challenging. Therefore, a comprehensive understanding of contextual information w.r.t. the aspect and opinion are further required in ABSA. In this paper, we propose Deep Contextualized Relation-Aware Network (DCRAN), which allows interactive relations among subtasks with deep contextual information based on two modules (i.e., Aspect and Opinion Propagation and Explicit Self-Supervised Strategies). Especially, we design novel self-supervised strategies for ABSA, which have strengths in dealing with multiple aspects. Experimental results show that DCRAN significantly outperforms previous state-of-the-art methods by large margins on three widely used benchmarks.