Beyond Polarity: Continuous Affect-Enhanced Multimodal Aspect-Based Sentiment Classification

ACL ARR 2026 January Submission8303 Authors

Keywords: Multimodal Aspect-based Sentiment Classification, Valence–Arousal–Dominance, Aspect-level Sentiment Reasoning

Abstract: 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 $\textbf{\textit{VADE}}$, a Valence–Arousal–Dominance $\textbf{\textit{VAD}}-\textbf{\textit{E}}$nhanced 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 this process, we construct an affect-enriched image–text dataset $\textbf{\textit{Senti-COCO}}$ 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 $\textbf{\textit{VADE}}$ achieves a new state-of-the-art performance, demonstrating the effectiveness of incorporating VAD signals for MABSC.

Paper Type: Long

Research Area: Sentiment Analysis, Stylistic Analysis, and Argument Mining

Research Area Keywords: emotion detection and analysis, language resources

Contribution Types: Publicly available software and/or pre-trained models, Data resources

Languages Studied: English

Submission Number: 8303