GateMABSA: Aspect-Image Gated Fusion for Multimodal Aspect-based Sentiment Analysis
This work addresses challenges in analyzing sentiment from text and images on social media, representing an incremental improvement in multimodal ABSA.
The paper tackled the problem of noisy visual signals and cross-modal alignment in multimodal aspect-based sentiment analysis by proposing GateMABSA, a gated architecture with specialized mLSTMs, which outperformed baselines on two Twitter datasets.
Aspect-based Sentiment Analysis (ABSA) has recently advanced into the multimodal domain, where user-generated content often combines text and images. However, existing multimodal ABSA (MABSA) models struggle to filter noisy visual signals, and effectively align aspects with opinion-bearing content across modalities. To address these challenges, we propose GateMABSA, a novel gated multimodal architecture that integrates syntactic, semantic, and fusion-aware mLSTM. Specifically, GateMABSA introduces three specialized mLSTMs: Syn-mLSTM to incorporate syntactic structure, Sem-mLSTM to emphasize aspect--semantic relevance, and Fuse-mLSTM to perform selective multimodal fusion. Extensive experiments on two benchmark Twitter datasets demonstrate that GateMABSA outperforms several baselines.