Multimodal Sentiment Analysis (MSA) endeavors to understand human sentiment
by leveraging language, visual, and acoustic modalities. Despite the remarkable
performance exhibited by previous MSA approaches, the presence of inherent
multimodal heterogeneities poses a challenge, with the contribution of
different modalities varying considerably. Past research predominantly focused
on improving representation learning techniques and feature fusion strategies.
However, many of these efforts overlooked the variation in semantic richness
among different modalities, treating each modality uniformly. This approach may
lead to underestimating the significance of strong modalities while
overemphasizing the importance of weak ones. Motivated by these insights, we
introduce a Text-oriented Cross-Attention Network (TCAN), emphasizing the
predominant role of the text modality in MSA. Specifically, for each multimodal
sample, by taking unaligned sequences of the three modalities as inputs, we
initially allocate the extracted unimodal features into a visual-text and an
acoustic-text pair. Subsequently, we implement self-attention on the text
modality and apply text-queried cross-attention to the visual and acoustic
modalities. To mitigate the influence of noise signals and redundant features,
we incorporate a gated control mechanism into the framework. Additionally, we
introduce unimodal joint learning to gain a deeper understanding of homogeneous
emotional tendencies across diverse modalities through backpropagation.
Experimental results demonstrate that TCAN consistently outperforms
state-of-the-art MSA methods on two datasets (CMU-MOSI and CMU-MOSEI).

多模态情感分析的研究中，我们引入了一个以文本为导向的交叉注意力网络（TCAN），强调文本在情感分析中的主导作用。通过对多模态样本进行自注意力和文本查询交叉注意力操作，我们减少了噪声信号和冗余特征的影响，并通过背向传播的方法实现了异质情感倾向的深入理解。实验证明，TCAN 在 CMU-MOSI 和 CMU-MOSEI 两个数据集上始终优于最先进的多模态情感分析方法。