The Limits of ChatGPT in Extracting Aspect-Category-Opinion-Sentiment
  Quadruples: A Comparative Analysis
Xiancai Xu Jia-Dong Zhang Rongchang Xiao Lei Xiong
Recently, ChatGPT has attracted great attention from both industry and
academia due to its surprising abilities in natural language understanding and
generation. We are particularly curious about whether it can achieve promising
performance on one of the most complex tasks in aspect-based sentiment
analysis, i.e., extracting aspect-category-opinion-sentiment quadruples from
texts. To this end, in this paper we develop a specialized prompt template that
enables ChatGPT to effectively tackle this complex quadruple extraction task.
Further, we propose a selection method on few-shot examples to fully exploit
the in-context learning ability of ChatGPT and uplift its effectiveness on this
complex task. Finally, we provide a comparative evaluation on ChatGPT against
existing state-of-the-art quadruple extraction models based on four public
datasets and highlight some important findings regarding the capability
boundaries of ChatGPT in the quadruple extraction.