Aspect-based Sentiment Analysis with Opinion Tree Generation

Abstract

Existing studies usually extract the sentiment elements by decomposing the complex structure prediction task into multiple subtasks. Despite their effectiveness, these methods ignore the semantic structure in ABSA problems and require extensive task-specific designs. In this study, we introduce a new Opinion Tree Generation model, which aims to jointly detect all sentiment elements in a tree. The opinion tree can reveal a more comprehensive and complete aspect-level sentiment structure. Furthermore, we employ a pre-trained model to integrate both syntax and semantic features for opinion tree generation. On one hand, a pre-trained model with large-scale unlabeled data is important for the tree generation model. On the other hand, the syntax and semantic features are very effective for forming the opinion tree structure. Extensive experiments show the superiority of our proposed method. The results also validate the tree structure is effective to generate sentimental elements.