  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.
