A Text-Centered Shared-Private Framework via Cross-Modal Prediction for Multimodal Sentiment Analysis

Multimodal fusion is a core problem for multimodal sentiment analysis. Previous works usually treat all three modal features equally and implicitly explore the interactions between different modalities. In this paper, we break this kind of methods in two ways. Firstly, we observe that textual modality plays the most important role in multimodal sentiment analysis, and this can be seen from the previous works. Secondly, we observe that comparing to the textual modality, the other two kinds of nontextual modalities (visual and acoustic) can provide two kinds of semantics, shared and private semantics. The shared semantics from the other two modalities can obviously enhance the textual semantics and make the sentiment analysis model more robust, and the private semantics can be complementary to the textual semantics and meanwhile provide different views to improve the performance of sentiment analysis together with the shared semantics. Motivated by these two observations, we propose a text-centered shared-private framework (TCSP) for multimodal fusion, which consists of the cross-modal prediction and sentiment regression parts. Experiments on the MOSEI and MOSI datasets demonstrate the effectiveness of our shared-private framework, which outperforms all baselines. Furthermore, our approach provides a new way to utilize the unlabeled data for multimodal sentiment analysis.