Aspect-based Sentiment Analysis with Opinion Tree Generation
Xiaoyi Bao, Zhongqing Wang, Xiaotong Jiang, Rong Xiao, Shoushan Li
Abstract
Existing studies usually extract these 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 an Opinion Tree Generation task, which aims to jointly detect all sentiment elements in a tree. We believe that the opinion tree can reveal a more comprehensive and complete aspect-level sentiment structure. Furthermore, we propose 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.