Abstract
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.
Abstract (translated)
面向 aspect 的情感分析 (ASSA) 任务涉及从句子中提取细粒度情感元组，旨在辨别作者的观点。传统的机器学习方法主要依赖监督方法；然而，在缺乏标注数据资源的低资源领域，这些方法的效力减弱，因为它们往往无法跨越领域。为了应对这个挑战，我们提出了一种简单而新颖的无监督方法，用于从句子中提取方面词的 opinions 和相应的情感极性。我们对四个基准数据集的实验评估表明，提取面向方面的情感词以及分配情感极性具有令人信服的表现。此外，面向情感词挖掘的无监督方法尚未被探索过，我们的工作为相同建立了基准。
URL
https://arxiv.org/abs/2404.13751
