arXiv:2404.04545v1 Announce Type: new
Abstract: 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).
Multimodal Sentiment Analysis: Understanding Human Sentiment Across Modalities
As technology continues to advance, multimedia information systems, animations, artificial reality, augmented reality, and virtual realities are becoming increasingly prevalent in our everyday lives. One area where these technologies play a crucial role is in the field of multimodal sentiment analysis (MSA).
MSA aims to understand human sentiment by leveraging multiple modalities such as language, visual cues, and acoustic signals. However, the presence of inherent multimodal heterogeneities poses a challenge, with the contribution of different modalities varying considerably. This has led researchers to focus on improving representation learning techniques and feature fusion strategies.
Nevertheless, many previous efforts have overlooked the variation in semantic richness among different modalities, treating each modality uniformly. This approach can lead to underestimating the significance of strong modalities while overemphasizing the importance of weak ones. In light of these insights, the authors of this article propose a Text-oriented Cross-Attention Network (TCAN) to address these limitations.
The TCAN model takes unaligned sequences of the three modalities as inputs and allocates the extracted unimodal features into a visual-text and an acoustic-text pair. It then implements self-attention on the text modality and applies text-queried cross-attention to the visual and acoustic modalities. Through a gated control mechanism, the model mitigates the influence of noise signals and redundant features.
Furthermore, the authors introduce the concept of unimodal joint learning, which aims to gain a deeper understanding of homogeneous emotional tendencies across diverse modalities through backpropagation. By considering the unique properties and strengths of each modality, TCAN outperforms state-of-the-art MSA methods on two datasets (CMU-MOSI and CMU-MOSEI).
The importance of this research extends beyond the field of MSA. The multi-disciplinary nature of the concepts explored in this article highlights the interconnectedness of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. The insights gained from this research can have implications in developing more efficient and accurate sentiment analysis models across various domains.
In conclusion, the Text-oriented Cross-Attention Network (TCAN) presented in this article showcases the significance of considering the variation in semantic richness among different modalities in multimodal sentiment analysis. By emphasizing the role of the text modality and incorporating innovative techniques, TCAN outperforms existing methods and contributes to the broader field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.