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.
翻译:结构性情绪分析试图从文本中提取完整的见解图,但随着时间的推移,这项任务被细分为较小和较小的子任务,例如目标提取或目标极分分类。我们争辩说,这一划分已经适得其反,并提出了一个新的统一框架来纠正这种情况。我们把结构化情绪问题作为依赖性图解析,其中的节点是情绪持有者、目标和表达方式,弧线是它们之间的关系。我们用四种语言(英语、挪威语、巴斯克语和加泰罗尼亚语)对五个数据集进行了实验,并表明这一方法大大改进了最新基线。我们的分析表明,用合成依赖性信息来完善情绪图会进一步改善结果。