New Benchmark Dataset and Fine-Grained Cross-Modal Fusion Framework for Vietnamese Multimodal Aspect-Category Sentiment Analysis
<|reference_start|>New Benchmark Dataset and Fine-Grained Cross-Modal Fusion Framework for Vietnamese Multimodal Aspect-Category Sentiment Analysis: 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.<|reference_end|>
@article{nguyen2024new,
    title={New Benchmark Dataset and Fine-Grained Cross-Modal Fusion Framework for
  Vietnamese Multimodal Aspect-Category Sentiment Analysis},
    author={Quy Hoang Nguyen, Minh-Van Truong Nguyen, Kiet Van Nguyen},
    journal={arXiv preprint arXiv:2405.00543},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.00543},
    primaryClass={cs.CL cs.AI}
}