CARBD-Ko: A Contextually Annotated Review Benchmark Dataset for Aspect-Level Sentiment Classification in Korean
<|reference_start|>CARBD-Ko: A Contextually Annotated Review Benchmark Dataset for Aspect-Level Sentiment Classification in Korean: 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.<|reference_end|>
@article{jang2024carbd-ko:,
    title={CARBD-Ko: A Contextually Annotated Review Benchmark Dataset for
  Aspect-Level Sentiment Classification in Korean},
    author={Dongjun Jang, Jean Seo, Sungjoo Byun, Taekyoung Kim, Minseok Kim,
  Hyopil Shin},
    journal={arXiv preprint arXiv:2402.15046},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.15046},
    primaryClass={cs.CL}
}