Multimodal Multi-loss Fusion Network for Sentiment Analysis

• URL: http://arxiv.org/abs/2308.00264v4
• Date: Sun, 2 Jun 2024 19:12:57 GMT
• Title: Multimodal Multi-loss Fusion Network for Sentiment Analysis
• Authors: Zehui Wu, Ziwei Gong, Jaywon Koo, Julia Hirschberg,
• Abstract summary: This paper investigates the optimal selection and fusion of feature encoders across multiple modalities to improve sentiment detection. We compare different fusion methods and examine the impact of multi-loss training within the multi-modality fusion network. We have found that integrating context significantly enhances model performance.
• Score: 3.8611070161950902
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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