Hierarchical Fusion Network with Enhanced Knowledge and Contrastive Learning for Multimodal Aspect-Based Sentiment Analysis on Social Media

Abstract

Aspect-based sentiment analysis (ABSA) is a task of fine-grained sentiment analysis that aims to determine the sentiment of a given target. With the increased prevalence of smart devices and social media, diverse data modalities have become more abundant. This fuels interest in multimodal ABSA (MABSA). However, most existing methods for MABSA prioritize analyzing the relationship between aspect–text and aspect–image, overlooking the semantic gap between text and image representations. Moreover, they neglect the rich information in external knowledge, e.g., image captions. To address these limitations, in this paper, we propose a novel hierarchical framework for MABSA, known as HF-EKCL, which also offers perspectives on sensor development within the context of sentiment analysis. Specifically, we generate captions for images to supplement the textual and visual features. The multi-head cross-attention mechanism and graph attention neural network are utilized to capture the interactions between modalities. This enables the construction of multi-level aspect fusion features that incorporate element-level and structure-level information. Furthermore, for this paper, we integrated modality-based and label-based contrastive learning methods into our framework, making the model learn shared features that are relevant to the sentiment of corresponding words in multimodal data. The results, based on two Twitter datasets, demonstrate the effectiveness of our proposed model.