Incorporating Multimodal Commonsense and Heterogeneous User Knowledge for Personalized Implicit Sentiment Analysis in Chinese

Implicit sentiment analysis (ISA) is particularly sensitive to user characteristics due to the absence of explicit sentiment cues. While existing approaches leverage explicit user attributes and social relationships, they neglect the implicit interest preferences embedded in user content and multimodal commonsense knowledge. This article introduces a novel personalized ISA framework that systematically integrates heterogeneous user knowledge with multimodal commonsense to address this limitation. Our core innovation lies in a multi-stage knowledge integration pipeline that first captures rich semantic representations through a large language model, then constructs a comprehensive user profile by fusing multiple views of implicit interests derived from user-multimodal commonsense-content interactions. Specifically, we employ graph neural networks to distill structured knowledge from automatically constructed multimodal commonsense graphs, which enhances semantic understanding. The different perspectives of user interests are then systematically fused to capture implicit preference characteristics. Finally, we introduce an adaptive gated fusion mechanism that dynamically incorporates heterogeneous user knowledge and multimodal commonsense into implicit sentiment semantics, enabling personalized analysis capabilities. Extensive experiments on two public personalized ISA Chinese datasets demonstrate that our method outperforms baselines by at least 2.86% and 3.03%, respectively, validating its effectiveness in comprehensive and personalized modeling of implicit sentiment.