  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.
