RVISA: Reasoning and Verification for Implicit Sentiment Analysis

TL;DR

This paper introduces RVISA, a two-stage framework combining decoder-only and encoder-decoder large language models to improve implicit sentiment analysis through explicit reasoning and verification, achieving state-of-the-art results.

Contribution

It proposes a novel two-stage reasoning framework that leverages the strengths of DO and ED LLMs for reliable implicit sentiment analysis.

Findings

Achieved state-of-the-art performance on benchmark datasets.

Effectively utilizes three-hop reasoning prompting for sentiment cues.

Introduced a verification mechanism to ensure reasoning reliability.

Abstract

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.