Harnessing Commonsense: LLM-Driven Knowledge Integration for Fine-Grained Sentiment Analysis

Kai Zhang, Yupeng Han

Abstract:

Fine-grained sentiment analysis, which aims to identify sentiments associated with specific aspects within sentences, faces challenges in effectively incorporating commonsense knowledge. Recent advancements leveraging large language models (LLMs) as data generators show promise but are limited by the LLMs’ lack of nuanced, domain-specific understanding and pose a significant risk of data leakage during inference, potentially leading to inflated performance metrics. To address these limitations, we propose LLM-Kit, a novel framework for commonsense-enhanced fine-grained sentiment analysis that integrates knowledge via LLM-guided graph construction, effectively mitigating data leakage risks. LLM-Kit operates in two key stages: (1) Commonsense Graph Construction
(\textbf{CGC}): We design second-order rules and leverage LLMs for evaluation to ensure the accuracy of the generated graph and mitigate the risk of data leakage from LLMs. (2) Knowledge-integration Graph Representation Learning (\textbf{KGRL}): We extract knowledge that is aware of various aspects through Graph Representation Learning (GRL). To capture the underlying semantic nuances within the input sentence, we develop a Sentence Semantic Learning (SSL) module based on RoBERTa that explicitly encodes internal semantics. This module provides complementary information to the GCN, improving the model’s ability to discern subtle sentiment variations related to different aspects. Comprehensive experiments on three public datasets affirm that LLM-Kit achieves comparable performance with state-of-the-art models.