Exploiting Diverse Feature for Multimodal Sentiment Analysis
Jia Li Wei Qian Kun Li Qi Li Dan Guo Meng Wang
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. \textbf{As a result, we achieved 3rd place in the
MuSe-Personalisation sub-challenge.} Specifically, we achieve the results of
0.8492 and 0.8439 for MuSe-Personalisation in terms of arousal and valence CCC.