Structured sentiment analysis attempts to extract full opinion tuples from a
text, but over time this task has been subdivided into smaller and smaller
sub-tasks, e,g,, target extraction or targeted polarity classification. We
argue that this division has become counterproductive and propose a new unified
framework to remedy the situation. We cast the structured sentiment problem as
dependency graph parsing, where the nodes are spans of sentiment holders,
targets and expressions, and the arcs are the relations between them. We
perform experiments on five datasets in four languages (English, Norwegian,
Basque, and Catalan) and show that this approach leads to strong improvements
over state-of-the-art baselines. Our analysis shows that refining the sentiment
graphs with syntactic dependency information further improves results.

本文提出了一种新的统一框架，将结构化情感分析问题作为依存关系图解析，其中节点是情感持有者、目标和表达式，弧是它们之间的关系。在四种语言的五个数据集上进行实验，结果显示这种方法比现有技术有更好的表现，并通过依存语法信息改进情感图产生了更好的结果。