With an increasing social demand for fine-grained sentiment analysis (SA), implicit sentiment analysis (ISA) poses a significant challenge with the absence of salient cue words in expressions. It necessitates reliable reasoning to understand how the sentiment is aroused and thus determine implicit sentiments. In the era of Large Language Models (LLMs), Encoder-Decoder (ED) LLMs have gained popularity to serve as backbone models for SA applications, considering impressive text comprehension and reasoning ability among diverse tasks. On the other hand, Decoder-only (DO) LLMs exhibit superior natural language generation and in-context learning capabilities. However, their responses may contain misleading or inaccurate information. To identify implicit sentiment with reliable reasoning, this study proposes RVISA, a two-stage reasoning framework that harnesses the generation ability of DO LLMs and the reasoning ability of ED LLMs to train an enhanced reasoner. Specifically, we adopt three-hop reasoning prompting to explicitly furnish sentiment elements as cues. The generated rationales are utilized to fine-tune an ED LLM into a skilled reasoner. Additionally, we develop a straightforward yet effective verification mechanism to ensure the reliability of the reasoning learning. We evaluated the proposed method on two benchmark datasets and achieved state-of-the-art results in ISA performance.

翻译：随着社会对细粒度情感分析（SA）需求的日益增长，隐式情感分析（ISA）因表达中缺乏显著线索词而构成重大挑战。它需要可靠的推理来理解情感如何被唤起，从而判定隐式情感。在大语言模型（LLM）时代，鉴于其在多种任务中展现出卓越的文本理解与推理能力，编码器-解码器（ED）架构的LLM已成为SA应用的主流骨干模型。另一方面，仅解码器（DO）架构的LLM则表现出更优异的自然语言生成和上下文学习能力，但其响应可能包含误导性或不准确的信息。为通过可靠推理识别隐式情感，本研究提出RVISA，一个两阶段推理框架，它利用DO LLM的生成能力和ED LLM的推理能力来训练一个增强型推理器。具体而言，我们采用三跳推理提示来显式地提供情感元素作为线索。生成的推理依据被用于微调一个ED LLM，使其成为熟练的推理器。此外，我们开发了一种简单而有效的验证机制，以确保推理学习的可靠性。我们在两个基准数据集上评估了所提方法，并在ISA性能上取得了最先进的结果。