Multimodal aspect-based sentiment analysis (MABSA) aims to identify aspect-level sentiments by jointly modeling textual and visual information, which is essential for fine-grained opinion understanding in social media. Existing approaches mainly rely on discriminative classification with complex multimodal fusion, yet lacking explicit sentiment explainability. In this paper, we reformulate MABSA as a generative and explainable task, proposing a unified framework that simultaneously predicts aspect-level sentiment and generates natural language explanations. Based on multimodal large language models (MLLMs), our approach employs a prompt-based generative paradigm, jointly producing sentiment and explanation. To further enhance aspect-oriented reasoning capabilities, we propose a dependency-syntax-guided sentiment cue strategy. This strategy prunes and textualizes the aspect-centered dependency syntax tree, guiding the model to distinguish different sentiment aspects and enhancing its explainability. To enable explainability, we use MLLMs to construct new datasets with sentiment explanations to fine-tune. Experiments show that our approach not only achieves consistent gains in sentiment classification accuracy, but also produces faithful, aspect-grounded explanations.

翻译：多模态方面级情感分析（MABSA）旨在通过联合建模文本与视觉信息来识别方面级情感，这对于社交媒体中的细粒度观点理解至关重要。现有方法主要依赖于复杂多模态融合的判别式分类，但缺乏明确的情感可解释性。本文中，我们将MABSA重新构建为一项生成式且可解释的任务，提出了一个统一框架，可同时预测方面级情感并生成自然语言解释。基于多模态大语言模型（MLLMs），我们的方法采用基于提示的生成范式，联合生成情感与解释。为进一步增强面向方面的推理能力，我们提出了一种依赖句法引导的情感线索策略。该策略对以方面为中心的依赖句法树进行剪枝与文本化，引导模型区分不同情感方面并增强其可解释性。为实现可解释性，我们利用MLLMs构建了带有情感解释的新数据集进行微调。实验表明，我们的方法不仅在情感分类准确率上取得了一致的提升，而且能生成忠实、基于方面的解释。