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.

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，并以图结构形式充分表示这些信息。最近提出的一种最精确的SSA方法是将该任务视为依存句法分析。尽管文献表明基于转移的算法在依存句法分析的准确性和效率方面表现出色，但所有基于该方法的SSA尝试都依赖于基于图的模型。本文首次提出了一种基于转移的方法，将SSA作为依存句法分析来处理。具体而言，我们设计了一个转移系统，通过从左到右的扫描方式逐步处理输入文本，增量式地生成包含所有已识别观点的图结构。为了有效实现最终的基于转移的模型，我们采用指针网络（Pointer Network）架构作为主干。通过广泛评估，我们证明该模型在几乎所有先前基于依存的方法中提供了迄今为止最佳的性能，并在最具挑战性的数据集上超越了近期特定任务的技术。此外，我们进行了深入分析，并通过实验证明，该方法的时间复杂度与句子长度呈二次关系，比性能最优的基于图的解析器更高效。