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 graph parsing task. Although we can find in the literature how transition-based algorithms excel in different dependency graph parsing tasks 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 graph 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 surpasses recent task-specific techniques on the most challenging datasets. We additionally include an in-depth analysis and empirically prove that the average-case time complexity of our approach is quadratic in the sentence length, being more efficient than top-performing graph-based parsers.

翻译：结构化情感分析（SSA）旨在从自然语言文本中自动提取人们的观点，并以图结构恰当地表示该信息。近期提出的最精确SSA方法之一，是将该任务视为依存图解析问题进行处理。尽管文献表明基于转移的算法在不同依存图解析任务中均展现出优异的准确性与效率，但所有遵循该路径解决SSA的现有尝试均基于基于图的模型。本文首次提出基于转移的方法，以依存图解析形式处理SSA。具体而言，我们设计了一个转移系统，通过从左到右的单次扫描处理输入文本，逐步生成包含所有识别观点的图结构。为有效实现最终的基于转移模型，我们采用指针网络架构作为主干网络。通过广泛评估，我们证明该模型在几乎所有情况下均优于现有基于依存的方法，并在最具挑战性的数据集上超越了近期任务专用技术。我们还进行了深入分析，并通过实验验证本方法的平均时间复杂度与句子长度呈二次关系，较当前性能最优的基于图解析器具有更高效率。