CARBD-Ko: A Contextually Annotated Review Benchmark Dataset for
  Aspect-Level Sentiment Classification in Korean
Dongjun Jang Jean Seo Sungjoo Byun Taekyoung Kim Minseok Kim Hyopil Shin
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.