Abstract
We introduce AdaptiSent, a new framework for Multimodal Aspect-Based Sentiment Analysis (MABSA) that uses adaptive cross-modal attention mechanisms to improve sentiment classification and aspect term extraction from both text and images. Our model integrates dynamic modality weighting and context-adaptive attention, enhancing the extraction of sentiment and aspect-related information by focusing on how textual cues and visual context interact. We tested our approach against several baselines, including traditional text-based models and other multimodal methods. Results from standard Twitter datasets show that AdaptiSent surpasses existing models in precision, recall, and F1 score, and is particularly effective in identifying nuanced inter-modal relationships that are crucial for accurate sentiment and aspect term extraction. This effectiveness comes from the model's ability to adjust its focus dynamically based on the context's relevance, improving the depth and accuracy of sentiment analysis across various multimodal data sets. AdaptiSent sets a new standard for MABSA, significantly outperforming current methods, especially in understanding complex multimodal information.
Abstract (translated)
我们介绍了AdaptiSent，这是一个新的多模态基于方面的情感分析（MABSA）框架。该框架采用自适应跨模式注意力机制来提高从文本和图像中提取情感分类和方面术语的效果。我们的模型整合了动态模态加权和上下文自适应注意功能，通过关注文字线索与视觉背景的相互作用，增强了情感和相关方面的信息抽取。 我们把这种方法与其他基线方法进行了对比测试，包括传统的基于文本的方法和其他多模态方法。在标准Twitter数据集上的结果显示，AdaptiSent在精度、召回率和F1分数上均超越了现有的模型，并且特别擅长识别对于准确的情感和方面术语提取至关重要的细微的跨模式关系。 这种有效性来源于该模型根据上下文的相关性动态调整其关注焦点的能力，从而提高了各种多模态数据集中的情感分析的深度和准确性。AdaptiSent为MABSA设定了新的标准，在理解复杂多模态信息方面显著优于现有的方法。
URL
https://arxiv.org/abs/2507.12695
