  Sentiment analysis, mostly based on text, has been rapidly developing in the
last decade and has attracted widespread attention in both academia and
industry. However, the information in the real world usually comes from
multiple modalities, such as audio and text. Therefore, in this paper, based on
audio and text, we consider the task of multimodal sentiment analysis and
propose a novel fusion strategy including both multi-feature fusion and
multi-modality fusion to improve the accuracy of audio-text sentiment analysis.
We call it the DFF-ATMF (Deep Feature Fusion - Audio and Text Modality Fusion)
model, which consists of two parallel branches, the audio modality based branch
and the text modality based branch. Its core mechanisms are the fusion of
multiple feature vectors and multiple modality attention. Experiments on the
CMU-MOSI dataset and the recently released CMU-MOSEI dataset, both collected
from YouTube for sentiment analysis, show the very competitive results of our
DFF-ATMF model. Furthermore, by virtue of attention weight distribution
heatmaps, we also demonstrate the deep features learned by using DFF-ATMF are
complementary to each other and robust. Surprisingly, DFF-ATMF also achieves
new state-of-the-art results on the IEMOCAP dataset, indicating that the
proposed fusion strategy also has a good generalization ability for multimodal
emotion recognition.
