Parameter-Efficient Adaptation and Benchmarking of Large Vision-Language Models for Multimodal Aspect-Based Sentiment Analysis
Multimodal aspect-based sentiment analysis (MABSA) predicts the sentiment expressed toward a target aspect by jointly using textual and visual information, supporting fine-grained opinion analysis in product reviews, brand monitoring, and customer feedback. However, existing approaches remain sensitive to irrelevant visual regions, weak text–image alignment, and limited use of external knowledge. Motivated by these challenges, this study systematically evaluated two text-only large language models and four open-weight large vision-language models for aspect-level sentiment classification. The open-weight models were adapted using 4-bit quantized low-rank adaptation, while GPT-4o was assessed under zero-shot, one-shot, and five-shot in-context learning without parameter updates. Experiments were conducted on Twitter-2015, Twitter-2017, and the seven-domain MASAD dataset and evaluated using accuracy and macro-F1. Among the evaluated multimodal models, Qwen3-VL-8B-Instruct achieves the strongest performance, reaching 83.22% accuracy and 81.72% macro-F1 on Twitter-2015, 79.50% and 78.93% on Twitter-2017, and up to 99.84% and 99.83% in the Plant domain of MASAD. From a symmetry perspective, semantically aligned text–image–aspect inputs provide consistent cross-modal evidence, whereas shuffled images introduce asymmetric, symmetry-breaking information. The resulting performance degradation under shuffled-image ablation indicates that reliable aspect-level sentiment prediction depends on preserving cross-modal semantic correspondence. These findings demonstrate the effectiveness of parameter-efficient LVLM adaptation for MABSA.