Fine-grained opinion analysis of text provides a detailed understanding of expressed sentiments, including the addressed entity. Although this level of detail is sound, it requires considerable human effort and substantial cost to annotate opinions in datasets for training models, especially across diverse domains and real-world applications. We explore the feasibility of LLMs as automatic annotators for fine-grained opinion analysis, addressing the shortage of domain-specific labelled datasets. In this work, we use a declarative annotation pipeline. This approach reduces the variability of manual prompt engineering when using LLMs to identify fine-grained opinion spans in text. We also present a novel methodology for an LLM to adjudicate multiple labels and produce final annotations. After trialling the pipeline with models of different sizes for the Aspect Sentiment Triplet Extraction (ASTE) and Aspect-Category-Opinion-Sentiment (ACOS) analysis tasks, we show that LLMs can serve as automatic annotators and adjudicators, achieving high Inter-Annotator Agreement across individual LLM-based annotators. This reduces the cost and human effort needed to create these fine-grained opinion-annotated datasets.

翻译：文本的细粒度观点分析提供了对表达情感的详细理解，包括所涉及的实体。尽管这种细节水平是合理的，但为训练模型而标注数据集中的观点需要大量的人力投入和成本，尤其是在跨领域和实际应用中。我们探索了将大型语言模型（LLMs）作为细粒度观点分析的自动标注器的可行性，以解决领域特定标注数据集短缺的问题。在本工作中，我们采用了一种声明式标注流程。这种方法减少了在使用LLMs识别文本中细粒度观点跨度时手动提示工程的可变性。我们还提出了一种新颖的方法，使LLM能够仲裁多个标签并生成最终标注。在针对方面情感三元组提取（ASTE）和方面-类别-观点-情感（ACOS）分析任务，使用不同规模的模型对流程进行试验后，我们表明，LLMs可以作为自动标注器和仲裁器，在基于LLM的个体标注器之间实现较高的标注者间一致性。这降低了创建这些细粒度观点标注数据集所需的成本和人力投入。