【摘要】 方面级情感分析（aspect-based sentiment analysis, ABSA）旨在识别文本中用户对于特定方面所表达的观点信息，涉及方面词、意见词、情感极性等多种元素.现有研究大多关注独立任务，忽略了各元素间的特征交互，存在错误传播问题.基于多特征加权图卷积网络提出的情感三元组抽取方法将多个子任务联合建模；采用双仿射注意力模块捕捉词对间的关系概率分布，将文本语义、句法、位置等先验信息编码为多特征向量；利用图卷积操作实现多特征融合，最终实现方面术语-意见术语-情感极性的联合抽取.基于两组基准数据集进行评估实验，实验结果表明，多特征加权图卷积网络的情感三元组抽取方法有效缓解了流水线方法错误传播的状况，提升了三元组各元素间的特征交互，处理三元组抽取任务的能力显著优于现有基准模型.更多还原

【Abstract】 Aspect-based sentiment analysis(ABSA) aims to identify users’ opinions expressed about specific text aspects using elements such as aspect words, opinion words, and sentiment polarity. However, the existing research mainly focuses on individual tasks, which neglects feature interactions between different parts and causes error propagation issues. A sentiment triplet extraction method based on a multi-feature weighted graph convolutional network is proposed to jointly model multiple subtasks. Then, a double affine attention module is employed to capture the relational probability distribution among word pairs. Additionally, prior information such as text semantics, syntax, and location is encoded into multi-feature vectors. Finally, graph convolution operations are utilized for achieving multi-feature fusion and realizing the joint extraction of aspect term-opinion term-sentiment polarity. Through the estimation test based on 2 benchmark datasets, the experimental results reveal that the sentiment triplet extraction method based on a multi-feature weighted graph convolutional network can effectively alleviate the error propagation issues in pipeline methods. Moreover, feature interaction among each factor of the triplet set is proposed, and it is proved that the model in the current work performs much better than the previous benchmark model at triplet extraction.更多还原