Abstract
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.
Abstract (translated)
多模态基于方面的情感分析（MABSA）旨在通过联合建模文本和视觉信息来识别基于方面的观点，这对于社交媒体上细微差别的意见理解至关重要。现有方法主要依赖于复杂的多模态融合的判别性分类，但缺乏明确的情感可解释性。在本文中，我们将 MABSA 重新定义为一个生成性和可解释性的任务，并提出了一个统一的框架，该框架同时预测基于方面的观点并生成自然语言解释。我们的方法基于多模态大型语言模型（MLLM），采用基于提示的生成范式，联合产生情感和解释。为了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。这种策略通过修剪以方面为中心的依存句法树并将文本化，指导模型区分不同的情感方面并提高其可解释性。为了实现可解释性，我们使用 MLLMs 构建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性上取得了持续改进，而且还生成了忠实、基于方面的解释。
URL
https://arxiv.org/abs/2601.06848
