Cross-modal fine-grained alignment and fusion network for multimodal aspect-based sentiment analysis

Abstract

Multi-modal Aspect-based Sentiment Analysis (MABSA) aims to forecast the polarity of sentiment concerning aspects within a given sentence based on the correlation between the sentence and its accompanying image. Comprehending multi-modal sentiment expression requires strong cross-modal alignment and fusion ability. Previous state-of-the-art (SOTA) models fail to explicitly align valuable visual clues with aspect and sentiment information in textual representations and overlook the utilization of syntactic dependency information in the accompanying text modality. We present CoolNet (Cross-modal Fine-grained Alignment and Fusion Network) to boost the performance of visual-language models in seamlessly integrating vision and language information. Specifically, CoolNet starts by transforming an image into a textual caption and a graph structure, then dynamically aligns the semantic and syntactic information from both the input sentence and the generated caption, as well as models the object-level visual features. Finally, a cross-modal transformer is employed to fuse and model the inter-modality dynamics.This network boasts advanced cross-modal fine-grained alignment and fusion capabilities. On standard benchmarks such as Twitter-2015 and Twitter-2017, CoolNet consistently outperforms state-of-the-art algorithm FITE with notable improvements in accuracy and Macro-F1 values. Specifically, we observe an improvement in accuracy and Macro-F1 values by 1.43% and 1.38% for Twitter-2015, and 0.74% and 0.88% for Twitter-2017, respectively, demonstrating the superiority of our CoolNet architecture.