Textual Graph Representation With Syntactic Weighting for Implicit Sentiment Analysis
Shunxiang Zhang, Jiawei Li, Shuyu Li, Wenjie Duan, Zhongliang Wei, Kuan‐Ching Li · IEEE Transactions on Emerging Topics in Computational Intelligence · 2025
Implicit sentiment analysis seeks to identify and interpret the underlying sentiment within texts that lack explicit sentiment words, significantly enhancing the capabilities of opinion analysis. Current methods often overlook the impact of context-dependent sequential text with graph neural networks, leading to an inadequate semantic representation of the text. In this paper, we propose a textual graph representation method with syntactic weighting for implicit sentiment analysis. This method improves textual semantic association by modeling the graph structure of the word position relationship in the text. It integrates syntactic weighting with an attention mechanism and guides node interactions in the graph attention network to generate textual graph representation with enhanced semantic depth and richness. The word semantics are enriched by introducing external knowledge. The proposed model is compared with existing models on the public implicit sentiment dataset SMP2019-ECISA, the explicit sentiment dataset NLPCC2014-SC, and the self-built dataset containing both explicit and implicit sentiment. The experimental results show that the proposed method can not only efficiently identify implicit sentiment, but also achieve some generalization and robustness in explicit sentiment recognition.