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.

翻译：本文探讨了预训练语言模型中基于方面的情感分类所面临的挑战，特别关注上下文化和幻觉问题。为应对这些挑战，我们提出了CARBD-Ko（面向韩语基于方面的情感分类的上下文标注评论基准数据集），该数据集通过引入方面和双标记极性，区分了特定方面情感分类与无关方面情感分类。数据集包含标注了特定方面、方面极性、无关方面极性及方面强度的句子。为处理双标记方面极性问题，我们提出了一种采用孪生网络的新方法。实验结果表明，准确预测双极性存在固有困难，并凸显了上下文情感分析模型的重要性。CARBD-Ko数据集为方面级情感分类的未来研究提供了宝贵资源。