GateMABSA: Aspect-Image Gated Fusion for Multimodal Aspect-based Sentiment Analysis
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912615190495232 |
|---|---|
| author | Lawan, Adamu Yunusa, Haruna |
| author_facet | Lawan, Adamu Yunusa, Haruna |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25037 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GateMABSA: Aspect-Image Gated Fusion for Multimodal Aspect-based Sentiment Analysis Lawan, Adamu Yunusa, Haruna Computation and Language 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. |
| title | GateMABSA: Aspect-Image Gated Fusion for Multimodal Aspect-based Sentiment Analysis |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2509.25037 |