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Multimodal Sentiment Analysis On Mosi

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Model Name	Accuracy	Paper Title	Repository
MMMU-BA	82.31%	Contextual Inter-modal Attention for Multi-modal Sentiment Analysis	-
Multilogue-Net	81.19	Multilogue-Net: A Context-Aware RNN for Multi-modal Emotion Detection and Sentiment Analysis in Conversation
CHFusion	76.5%	Multimodal Sentiment Analysis using Hierarchical Fusion with Context Modeling
bc-LSTM	80.3%	Context-Dependent Sentiment Analysis in User-Generated Videos
CM-BERT	84.5%	Cross-Modal BERT for Text-Audio Sentiment Analysis
Proposed: B2 + B4 w/ multimodal fusion	83.91%	Gated Mechanism for Attention Based Multimodal Sentiment Analysis	-
UniMSE	86.9	UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition
MMML	90.35	Multimodal Multi-loss Fusion Network for Sentiment Analysis
MARN	77.1%	Multi-attention Recurrent Network for Human Communication Comprehension
MulT	83	Multimodal Transformer for Unaligned Multimodal Language Sequences
SPECTRA	87.50	Speech-Text Dialog Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal Alignment

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Multimodal Sentiment Analysis On Mosi | SOTA | HyperAI