Exploiting Diverse Feature for Multimodal Sentiment Analysis

In this paper, we present our solution to the MuSe-Personalisation sub-challenge in the MuSe 2023 Multimodal Sentiment Analysis Challenge. The task of MuSe-Personalisation aims to predict the continuous arousal and valence values of a participant based on their audio-visual, language, and physiological signal modalities data. Considering different people have personal characteristics, the main challenge of this task is how to build robustness feature presentation for sentiment prediction. To address this issue, we propose exploiting diverse features. Specifically, we proposed a series of feature extraction methods to build a robust representation and model ensemble. We empirically evaluate the performance of the utilized method on the officially provided dataset. $\mathbf{\text{As}}\mathbf{\text{a}}\mathbf{\text{result,}}\mathbf{\text{we}}\mathbf{\text{achieved}}\mathbf{\text{3rd}}\mathbf{\text{place}}\mathbf{\text{in}}\mathbf{\text{the}}\mathbf{\text{MuSe-Personalisation}}\mathbf{\text{sub-challenge.}}$ Specifically, we achieve the results of 0.8492 and 0.8439 for MuSe-Personalisation in terms of arousal and valence CCC.