  The emergence of multimodal data on social media platforms presents new
opportunities to better understand user sentiments toward a given aspect.
However, existing multimodal datasets for Aspect-Category Sentiment Analysis
(ACSA) often focus on textual annotations, neglecting fine-grained information
in images. Consequently, these datasets fail to fully exploit the richness
inherent in multimodal. To address this, we introduce a new Vietnamese
multimodal dataset, named ViMACSA, which consists of 4,876 text-image pairs
with 14,618 fine-grained annotations for both text and image in the hotel
domain. Additionally, we propose a Fine-Grained Cross-Modal Fusion Framework
(FCMF) that effectively learns both intra- and inter-modality interactions and
then fuses these information to produce a unified multimodal representation.
Experimental results show that our framework outperforms SOTA models on the
ViMACSA dataset, achieving the highest F1 score of 79.73%. We also explore
characteristics and challenges in Vietnamese multimodal sentiment analysis,
including misspellings, abbreviations, and the complexities of the Vietnamese
language. This work contributes both a benchmark dataset and a new framework
that leverages fine-grained multimodal information to improve multimodal
aspect-category sentiment analysis. Our dataset is available for research
purposes:
https://github.com/hoangquy18/Multimodal-Aspect-Category-Sentiment-Analysis.
