  Structured sentiment analysis (SSA) aims to automatically extract people's
opinions from a text in natural language and adequately represent that
information in a graph structure. One of the most accurate methods for
performing SSA was recently proposed and consists of approaching it as a
dependency parsing task. Although we can find in the literature how
transition-based algorithms excel in dependency parsing in terms of accuracy
and efficiency, all proposed attempts to tackle SSA following that approach
were based on graph-based models. In this article, we present the first
transition-based method to address SSA as dependency parsing. Specifically, we
design a transition system that processes the input text in a left-to-right
pass, incrementally generating the graph structure containing all identified
opinions. To effectively implement our final transition-based model, we resort
to a Pointer Network architecture as a backbone. From an extensive evaluation,
we demonstrate that our model offers the best performance to date in
practically all cases among prior dependency-based methods, and surpass recent
task-specific techniques on the most challenging datasets. We additionally
include an in-depth analysis and empirically prove that the overall
time-complexity cost of our approach is quadratic in the sentence length, being
more efficient than top-performing graph-based parsers.
