CARBD-Ko: A Contextually Annotated Review Benchmark Dataset for Aspect-Level Sentiment Classification in Korean

The effectiveness of various pretrained language models, including BERT [Devlin et al., 2018], XLNet [Yang et al., 2019], BART [Lewis et al., 2020], and GPT-3, in sentiment classification, a significant downstream task, has been extensively studied. Current research in sentiment classification often focuses on identifying sentiment polarities at the aspect level, leading to the emergence of aspect-based sentiment classification (ABSC). Many studies have achieved impressive results and introduced innovative approaches to tackle the ABSC task. For instance, Sun et al. [2019] utilized BERT to transform ABSC tasks into sentence-pair classification, which has influenced subsequent methodologies [Hu et al., 2022]. Additionally, generative models like BART [Lewis et al., 2020] have been employed by Yan et al. [2021] to convert ABSC tasks into sequence-to-sequence tasks, enabling the prediction of token sequences representing identified aspects and associated sentiments. Furthermore, Li et al. [2021a] reframed ABSC tasks as masked language modeling tasks, effectively bridging the performance gap between pre-training and ABSC tasks. Despite numerous attempts to address aspect-level sentiment classification, the primary focus has been on improving aspect-level sentiment polarity performance through specialized datasets and training methodologies. However, it is equally crucial for models to predict not only the in-context polarity of aspects but also their aspect polarity.