  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).
