Multimodal aspect-based sentiment classification (MABSC) requires aspect-level sentiment inference from textual-image data that jointly convey opinions. Yet most existing approaches primarily exploit discrete polarity patterns and generic visual embeddings, making them less effective when the affect is subtle, implicit, or expressed through imagery. In this work, we propose VADE, a Valence–Arousal–Dominance~(VAD)-Enhanced MABSC framework that brings continuous VAD signals into multimodal sentiment reasoning and learns emotion-sensitive image representations. Specifically, we design a VAD encoder to extract continuous affect cues from text for aspect-level sentiment reasoning. Furthermore, we fine-tune a CLIP-based image encoder on affect-enriched image–text pairs to obtain visual representations that are more sensitive to sentiment cues. To support the fine-tuning process, we construct an affect-enriched image–text dataset Senti-COCO by rewriting MSCOCO captions with a multimodal large language model, which yields large-scale image-text pairs with richer affective expressions. Experiments on two mainstream datasets, Twitter-15 and Twitter-17, show that VADE achieves a new state-of-the-art performance, demonstrating the effectiveness of incorporating VAD signals for MABSC.

Aspect-Based Sentiment Analysis (ABSA) is an important subtask in Natural Language Processing (NLP). More recent research within ABSA have consistently focused on conducting more precise sentiment analysis on aspects, i.e., dimensional Aspect-Based Sentiment Analysis (dimABSA). However, previous approaches have not systematically explored the use of Large Language Models (LLMs) in dimABSA. To fill the gap, we propose a novel In-Context Learning (ICL) structure with a novel aspect-aware ICL example selection method, to enhance the performance of LLMs in dimABSA. Experiments show that our proposed ICL structure significantly improves the fine-grained sentiment analysis abilities of LLMs.