This study examines the performance of Large Language Models (LLMs) in Aspect-Based Sentiment Analysis (ABSA), with a focus on implicit aspect extraction in a novel domain. Using a synthetic sports feedback dataset, we evaluate open-weight LLMs' ability to extract aspect-polarity pairs and propose a metric to facilitate the evaluation of aspect extraction with generative models. Our findings highlight both the potential and limitations of LLMs in the ABSA task.

翻译：本研究考察了大语言模型（LLMs）在方面级情感分析（ABSA）中的性能，重点关注其在新领域内隐式方面的提取能力。通过使用合成的体育反馈数据集，我们评估了开源权重LLMs提取方面-极性对的能力，并提出了一种便于评估生成模型方面提取性能的指标。我们的研究结果既揭示了LLMs在ABSA任务中的潜力，也指出了其局限性。