  This paper investigates the optimal selection and fusion of feature encoders
across multiple modalities and combines these in one neural network to improve
sentiment detection. We compare different fusion methods and examine the impact
of multi-loss training within the multi-modality fusion network, identifying
surprisingly important findings relating to subnet performance. We have also
found that integrating context significantly enhances model performance. Our
best model achieves state-of-the-art performance for three datasets (CMU-MOSI,
CMU-MOSEI and CH-SIMS). These results suggest a roadmap toward an optimized
feature selection and fusion approach for enhancing sentiment detection in
neural networks.
