Aspect Based Sentiment Analysis (ABSA) tasks involve the extraction of fine-grained sentiment tuples from sentences, aiming to discern the author's opinions. Conventional methodologies predominantly rely on supervised approaches; however, the efficacy of such methods diminishes in low-resource domains lacking labeled datasets since they often lack the ability to generalize across domains. To address this challenge, we propose a simple and novel unsupervised approach to extract opinion terms and the corresponding sentiment polarity for aspect terms in a sentence. Our experimental evaluations, conducted on four benchmark datasets, demonstrate compelling performance to extract the aspect oriented opinion words as well as assigning sentiment polarity. Additionally, unsupervised approaches for opinion word mining have not been explored and our work establishes a benchmark for the same.

翻译：基于方面的情感分析任务涉及从句子中提取细粒度的情感元组，旨在识别作者的观点。传统方法主要依赖于监督学习方法；然而，在缺乏标注数据的低资源领域中，此类方法的有效性会降低，因为它们往往缺乏跨领域的泛化能力。为解决这一挑战，我们提出了一种简单且新颖的无监督方法，用于提取句子中方面术语对应的观点词及其情感极性。我们在四个基准数据集上进行的实验评估表明，该方法在提取面向方面的观点词以及分配情感极性方面均表现出令人信服的性能。此外，无监督方法在观点词挖掘方面尚未被探索，而我们的工作为此建立了一个基准。