@inproceedings{yin-etal-2025-feature,
    title = "Feature Decomposition-Augmentation Network for Multimodal Sentiment Analysis",
    author = "Yin, Dapeng  and
      Hou, Bingxuan  and
      Gao, Mengna  and
      Zhu, Shuyue  and
      Wang, Junli",
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.ijcnlp-long.6/",
    pages = "86--98",
    ISBN = "979-8-89176-298-5",
    abstract = "Multimodal sentiment analysis identifies human emotional tendencies by analyzing text, visual, and auditory modalities. In most studies, the textual modality is usually considered to contain the most emotional information and is regarded as the dominant modality. Existing methods mostly map auxiliary modalities into a semantic space close to the dominant modality, which overly relies on the dominant modality. In this work, we propose a Feature Decomposition-Augmentation (FeaDA) framework, which aims to elevate the role of auxiliary modalities in multimodal data fusion. We first design a projector to decompose auxiliary modalities into partial features, which contain features for emotion judgment, and then utilize these decomposed features to guide the fusion process with KL loss, thereby enhancing the status of auxiliary modality fusion. To verify the effectiveness of our method, we conducted experiments on the CMU-MOSI, CMU-MOSEI, and CH-SIMS datasets. The experimental results show that our FeaDA framework outperforms mutilmodal sentiment analysis methods of the same type in main metrics. Our code is available at https://github.com/PowerLittleYin/FeaDA-main."
}