TDCSA : LLM -Guided Top-Down Approach for Robust Citation Sentiment Analysis (2025)

Abstract: Citation 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. % importance & limitations 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 bottom-up feature engineering paradigm into a top-down architecture. % what we do 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. % how we do Experiments demonstrate that TDCSA significantly outperforms existing methods, achieving state-of-the-art performance while maintaining robustness to quadruple quality variations.

## introduction citations play a fundamental role in academic contributions, reflecting complex relationships and attitudes between scholarly works @xcite . while sentiment analysis has advanced significantly in domains like consumer reviews and social media posts, citation sentiment analysis (csa) presents unique challenges. scientific citations employ formal and technical language, emphasizing objective descriptions over explicit evaluations, making sentiment expressions often implicit and requiring deeper semantic understanding @xcite . for instance in figure 1 , figure 1 : the key limitations of traditional plms in csa. while baseline models struggle to identify implicit sentiment expressions in academic writing, our method leverages llm-extracted quadruples to enhance semantic understanding. even with varying quadruple quality (e.g., the low-confidence quadruple), the model maintains robust performance. machine learning approaches to csa have followed a bottom-up paradigm, where researchers carefully design and combine low-level linguistic features such as n-grams, dependency relations, or sentiment lexicons to capture sentiments @xcite @xcite . these feature engineering methods have achieved considerable success through domain expertise and manual effort @xcite . the evolution of deep learning brought innovations through word embeddings @xcite and neural networks @xcite , followed by pre-trained language models (plms) and large language models (llms). plms like bert offer efficient fine-tuning capabilities for downstream tasks @xcite , while llms benefit from massive training corpora that enable better semantic comprehension of scientific text @xcite . however, current solutions face two significant limitations. 1. suboptimal performance: while traditional machine learning approaches with carefully engineered features remain competitive, particularly svm using dependency relations, domain-specific plms still face challenges in capturing implicit sentiment expressions in academic writing. as shown in figure 1 , plm struggles to capture subtle positive sentiment conveyed through phrases like "manageable behavior". while llms demonstrate better semantic comprehension, their direct application proves computationally intensive and potentially suboptimal for specific downstream tasks @xcite , as demonstrated in section c.2. in order to tackle these limitations, we propose a novel framework that leverages llms as robust feature generator to generate structured sentiment information, which then enhance smaller, taskspecific plms for sentiment classification. notably, our framework maintains robustness to quadruple quality variations, as it focuses on capturing general sentiment patterns rather than requiring perfect extraction accuracy. we design three components: a dual llm feature generation (dfg) framework that employs critical thinking @xcite and chain-of-thought @xcite reasoning to extract robust sentiment quadruples. a multi-view pattern representation (mpr) mechanism that handles the dominant neutral citations with complementary perspectives, avoiding feature redundancy. an quad feature enhanced plm (qfe-plm) that integrates llmextracted features with plm through adaptive fusion mechanisms. the main contributions of this paper can be summarized as follows: ## methodology the overall architecture of tdcsa is shown in the figure 3 : it consists of three key components ## conclusion in this paper we introduced tdcsa, a novel top-down approach designed to address the challenges in citation sentiment analysis. by leveraging llms for robust quadruple generation and enhancing pre-trained language models with adaptive fusion mechanisms, tdcsa achieved significant performance improvements over existing methods. our analysis yields three key findings: first, structured sentiment quadruples effectively extraction provides interpretable features that enhance citation sentiment detection, particularly for implicit senti-