TDCSA: LLM-Guided Top-Down Approach for Robust Citation Sentiment Analysis

Sentiment Analysis (CSA) plays a crucial role in understanding academic influence and knowledge diffusion.While pre-trained language models (PLMs) and large language models (LLMs) showed remarkable success in general sentiment analysis, they encounter specialized challenges in CSA due to the less significant and implicit sentiment expressions in academic writing, as well as complex sentiment transitions.In order to address the challenges, We propose TDCSA, a Top-Down framework that leverages LLMs' semantic understanding capabilities to enhance PLM-based CSA, which transforms the traditional bottomup feature engineering paradigm into a topdown architecture.Our framework consists of three key components: (1) a Dual LLM Feature Generation module for robust quadruple extraction, (2) a Multi-view Feature Representation mechanism for neutral citation processing, and (3) a Quad Feature Enhanced PLM.Experiments demonstrate that TDCSA 1 significantly outperforms existing methods, achieving state-of-the-art performance while maintaining robustness to quadruple quality variations.