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Multimodal Sentiment Analysis

Multimodal sentiment analysis is the task of performing sentiment analysis with multiple data sources - e.g. a camera feed of someone's face and their recorded speech.

( Image credit: ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection )

Papers

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Showing 101–125 of 202 papers

TitleStatusHype
Gated Mechanism for Attention Based Multimodal Sentiment Analysis—0
Generalizable Multi-linear Attention Network—0
Graph-based Fine-grained Multimodal Attention Mechanism for Sentiment Analysis—0
Hierarchical Adaptive Expert for Multimodal Sentiment Analysis—0
Highlight Timestamp Detection Model for Comedy Videos via Multimodal Sentiment Analysis—0
Hybrid Contrastive Learning of Tri-Modal Representation for Multimodal Sentiment Analysis—0
Improving Multimodal fusion via Mutual Dependency Maximisation—0
Improving Multimodal Sentiment Analysis: Supervised Angular Margin-based Contrastive Learning for Enhanced Fusion Representation—0
Improving the Modality Representation with Multi-View Contrastive Learning for Multimodal Sentiment Analysis—0
Large Language Models Meet Text-Centric Multimodal Sentiment Analysis: A Survey—0
Learning in Order! A Sequential Strategy to Learn Invariant Features for Multimodal Sentiment Analysis—0
Learning Relationships between Text, Audio, and Video via Deep Canonical Correlation for Multimodal Language Analysis—0
Learning Robust Joint Representations for Multimodal Sentiment Analysis—0
Lightweight Models for Multimodal Sequential Data—0
M2Lens: Visualizing and Explaining Multimodal Models for Sentiment Analysis—0
MART: Masked Affective RepresenTation Learning via Masked Temporal Distribution Distillation—0
Meta-Learn Unimodal Signals with Weak Supervision for Multimodal Sentiment Analysis—0
Missing Modality meets Meta Sampling (M3S): An Efficient Universal Approach for Multimodal Sentiment Analysis with Missing Modality—0
Modality Influence in Multimodal Machine Learning—0
Modality-Invariant Bidirectional Temporal Representation Distillation Network for Missing Multimodal Sentiment Analysis—0
Modeling Intra- and Inter-Modal Relations: Hierarchical Graph Contrastive Learning for Multimodal Sentiment Analysis—0
Multi-channel Attentive Graph Convolutional Network With Sentiment Fusion For Multimodal Sentiment Analysis—0
Multi-label Emotion Analysis in Conversation via Multimodal Knowledge Distillation—0
Multimodal Contrastive Learning via Uni-Modal Coding and Cross-Modal Prediction for Multimodal Sentiment Analysis—0
Multimodal fusion via cortical network inspired losses—0
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