Multimodal sentiment analysis (MSA) provides a novel way to understand human sentiments. However, the differences between distribution patterns across modalities bring challenges in this domain. The inconsistency of recognitions with different modalities leads to incorrect final results. Moreover, the gaps between sentiments with different degrees are small in one modality, but the gaps between sentiments with same degree are large across different modalities. The imbalance leads to incorrect recognition for different sentiment degrees. Since the fuzzy network shows excellent performance in integrating data from multiple modalities, this study constructs a fuzzy cognition-based dynamic fusion network (Fcdnet) for MSA. The Fcdnet dynamically integrates sentiment scores across different modalities using a fuzzy cognition fusion mechanism (FCM), significantly enhancing the accuracy of identifying divergent sentiments across modalities. Additionally, a disparity balancing module (DBM) is proposed to normalize the representations between different modality features by penalizing the similarity of sentiments with different degrees and rewarding the separability of sentiments with same degree. Experimental results demonstrate that Fcdnet outperforms state-of-the-art methods on public datasets, validating the superiority and effectiveness.

中文翻译：

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Fcdnet：用于多模态情感分析的基于模糊认知的动态融合网络


多模态情感分析（MSA）提供了一种理解人类情感的新方法。然而，不同模式之间的分配模式之间的差异给该领域带来了挑战。不同模态的识别不一致会导致最终结果不正确。此外，在一种模态中不同程度的情绪之间的差距很小，但在不同模态中相同程度的情绪之间的差距很大。这种不平衡导致对不同情感程度的错误识别。由于模糊网络在整合多模态数据方面表现出优异的性能，因此本研究构建了一种用于MSA的基于模糊认知的动态融合网络（Fcdnet）。 Fcdnet 使用模糊认知融合机制 (FCM) 动态集成不同模态的情感分数，显着提高了识别不同模态的不同情感的准确性。此外，提出了视差平衡模块（DBM），通过惩罚不同程度的情感相似性并奖励相同程度的情感可分离性来规范不同模态特征之间的表示。实验结果表明，Fcdnet 在公共数据集上优于最先进的方法，验证了其优越性和有效性。