This paper explores the challenges posed by aspect-based sentiment
classification (ABSC) within pretrained language models (PLMs), with a
particular focus on contextualization and hallucination issues. In order to
tackle these challenges, we introduce CARBD-Ko (a Contextually Annotated Review
Benchmark Dataset for Aspect-Based Sentiment Classification in Korean), a
benchmark dataset that incorporates aspects and dual-tagged polarities to
distinguish between aspect-specific and aspect-agnostic sentiment
classification. The dataset consists of sentences annotated with specific
aspects, aspect polarity, aspect-agnostic polarity, and the intensity of
aspects. To address the issue of dual-tagged aspect polarities, we propose a
novel approach employing a Siamese Network. Our experimental findings highlight
the inherent difficulties in accurately predicting dual-polarities and
underscore the significance of contextualized sentiment analysis models. The
CARBD-Ko dataset serves as a valuable resource for future research endeavors in
aspect-level sentiment classification.

本研究探讨了预训练语言模型中面向方面的情感分类（ABSC）所面临的挑战，特别关注上下文化和产生幻觉的问题。为了应对这些挑战，我们引入了 CARBD-Ko（一个上下文标注的韩文面向方面情感分类基准数据集），该数据集将方面和双标极性结合起来以区分面向特定和面向不可知的情感分类。该数据集包含用于注释特定方面、方面极性、面向不可知极性和方面强度的句子。为了解决双标方面极性的问题，我们提出了一种使用连体网络的新方法。我们的实验结果突出显示了准确预测双极性的困难，并强调了上下文化情感分析模型的重要性。CARBD-Ko 数据集为未来面向层面情感分类的研究提供了宝贵的资源。